From f816b099a88e000027b454350166978267099692 Mon Sep 17 00:00:00 2001
From: nexus <nexus@tesselle.org>
Date: Thu, 30 Nov 2023 10:15:39 +0000
Subject: [PATCH] Built site for nexus@9f78190

---
 nexus/articles/bibliography.html              |   2 +-
 nexus/articles/nexus.html                     |   2 +-
 .../figure-html/ceramics-pca-2.png            | Bin 66093 -> 65500 bytes
 nexus/deps/bootstrap-5.3.1/bootstrap.min.css  |   2 +-
 nexus/index.html                              |   1 +
 nexus/news/index.html                         |   2 +-
 nexus/pkgdown.yml                             |   2 +-
 nexus/reference/Rplot003.png                  | Bin 40859 -> 40467 bytes
 nexus/reference/Rplot004.png                  | Bin 20823 -> 19705 bytes
 nexus/reference/as_amounts.html               |  18 +++++++++---------
 nexus/reference/as_composition.html           |  18 +++++++++---------
 nexus/reference/as_features.html              |  18 +++++++++---------
 nexus/reference/as_graph-1.png                | Bin 56120 -> 57040 bytes
 nexus/reference/as_graph-2.png                | Bin 30127 -> 23473 bytes
 nexus/reference/as_graph-3.png                | Bin 56149 -> 57294 bytes
 nexus/reference/as_graph-4.png                | Bin 35474 -> 33445 bytes
 nexus/reference/mahalanobis.html              |   8 ++++----
 nexus/reference/outliers-3.png                | Bin 140402 -> 139586 bytes
 nexus/reference/outliers-4.png                | Bin 68717 -> 69012 bytes
 nexus/reference/pca-2.png                     | Bin 42918 -> 42815 bytes
 nexus/reference/plot_outliers-3.png           | Bin 141747 -> 138806 bytes
 nexus/reference/plot_outliers-4.png           | Bin 75335 -> 69699 bytes
 nexus/reference/totals.html                   |  18 +++++++++---------
 nexus/search.json                             |   2 +-
 24 files changed, 47 insertions(+), 46 deletions(-)

diff --git a/nexus/articles/bibliography.html b/nexus/articles/bibliography.html
index 3ea0283c2..c776698a7 100644
--- a/nexus/articles/bibliography.html
+++ b/nexus/articles/bibliography.html
@@ -91,7 +91,7 @@ <h3 data-toc-skip class="subtitle">Comprehensive list of
                         <h4 data-toc-skip class="author">N.
 Frerebeau</h4>
             
-            <h4 data-toc-skip class="date">2023-11-29</h4>
+            <h4 data-toc-skip class="date">2023-11-30</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/tesselle/nexus/blob/HEAD/vignettes/bibliography.Rmd" class="external-link"><code>vignettes/bibliography.Rmd</code></a></small>
       <div class="d-none name"><code>bibliography.Rmd</code></div>
diff --git a/nexus/articles/nexus.html b/nexus/articles/nexus.html
index 030fa27b1..4aab8b5f4 100644
--- a/nexus/articles/nexus.html
+++ b/nexus/articles/nexus.html
@@ -89,7 +89,7 @@
                         <h4 data-toc-skip class="author">N.
 Frerebeau</h4>
             
-            <h4 data-toc-skip class="date">2023-11-29</h4>
+            <h4 data-toc-skip class="date">2023-11-30</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/tesselle/nexus/blob/HEAD/vignettes/nexus.Rmd" class="external-link"><code>vignettes/nexus.Rmd</code></a></small>
       <div class="d-none name"><code>nexus.Rmd</code></div>
diff --git a/nexus/articles/nexus_files/figure-html/ceramics-pca-2.png b/nexus/articles/nexus_files/figure-html/ceramics-pca-2.png
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diff --git a/nexus/deps/bootstrap-5.3.1/bootstrap.min.css b/nexus/deps/bootstrap-5.3.1/bootstrap.min.css
index f0173fc80..182552e19 100644
--- a/nexus/deps/bootstrap-5.3.1/bootstrap.min.css
+++ b/nexus/deps/bootstrap-5.3.1/bootstrap.min.css
@@ -2,4 +2,4 @@
    * Bootstrap  v5.3.1 (https://getbootstrap.com/)
    * Copyright 2011-2023 The Bootstrap Authors
    * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
-   */:root,[data-bs-theme="light"]{--bs-blue: #007bc2;--bs-indigo: #4b00c1;--bs-purple: #74149c;--bs-pink: #bf007f;--bs-red: #c10000;--bs-orange: #f45100;--bs-yellow: #f9b928;--bs-green: #00891a;--bs-teal: #00bf7f;--bs-cyan: #03c7e8;--bs-black: #000;--bs-white: #fff;--bs-gray: #707782;--bs-gray-dark: #343A46;--bs-gray-100: #f8f8f8;--bs-gray-200: #e9ecef;--bs-gray-300: #dee2e6;--bs-gray-400: #cdd4da;--bs-gray-500: #8D959E;--bs-gray-600: #707782;--bs-gray-700: #48505F;--bs-gray-800: #343A46;--bs-gray-900: #1D1F21;--bs-default: #dee2e6;--bs-primary: #007bc2;--bs-secondary: #404040;--bs-success: #00891a;--bs-info: #03c7e8;--bs-warning: #f9b928;--bs-danger: #c10000;--bs-light: #f8f8f8;--bs-dark: #202020;--bs-default-rgb: 222,226,230;--bs-primary-rgb: 0,123,194;--bs-secondary-rgb: 64,64,64;--bs-success-rgb: 0,137,26;--bs-info-rgb: 3,199,232;--bs-warning-rgb: 249,185,40;--bs-danger-rgb: 193,0,0;--bs-light-rgb: 248,248,248;--bs-dark-rgb: 32,32,32;--bs-primary-text-emphasis: 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auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:var(--bs-border-radius)}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(var(--bs-border-width) * -1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n + 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-1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-link-color);--bs-nav-link-hover-color: var(--bs-link-hover-color);--bs-nav-link-disabled-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem rgba(0,123,194,0.25)}.nav-link.disabled,.nav-link:disabled{color:var(--bs-nav-link-disabled-color);pointer-events:none;cursor:default}.nav-tabs{--bs-nav-tabs-border-width: var(--bs-border-width);--bs-nav-tabs-border-color: var(--bs-border-color);--bs-nav-tabs-border-radius: var(--bs-border-radius);--bs-nav-tabs-link-hover-border-color: var(--bs-secondary-bg) var(--bs-secondary-bg) var(--bs-border-color);--bs-nav-tabs-link-active-color: var(--bs-emphasis-color);--bs-nav-tabs-link-active-bg: var(--bs-body-bg);--bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1 * var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid transparent;border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * 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.show>.nav-link{font-weight:700;color:var(--bs-nav-underline-link-active-color);border-bottom-color:currentcolor}.nav-fill>.nav-link,.nav-fill .nav-item{flex:1 1 auto;-webkit-flex:1 1 auto;text-align:center}.nav-justified>.nav-link,.nav-justified .nav-item{flex-basis:0;-webkit-flex-basis:0;flex-grow:1;-webkit-flex-grow:1;text-align:center}.nav-fill .nav-item .nav-link,.nav-justified .nav-item .nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x: .66rem;--bs-navbar-padding-y: .5rem;--bs-navbar-color: rgba(var(--bs-emphasis-color-rgb), 0.65);--bs-navbar-hover-color: rgba(var(--bs-emphasis-color-rgb), 0.8);--bs-navbar-disabled-color: rgba(var(--bs-emphasis-color-rgb), 0.3);--bs-navbar-active-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-padding-y: .3125rem;--bs-navbar-brand-margin-end: 1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-hover-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-nav-link-padding-x: .5rem;--bs-navbar-toggler-padding-y: .25rem;--bs-navbar-toggler-padding-x: .75rem;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%2829,31,33,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(var(--bs-emphasis-color-rgb), 0.15);--bs-navbar-toggler-border-radius: var(--bs-border-radius);--bs-navbar-toggler-focus-width: .25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) 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.navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out,border-radius 0.15s ease;--bs-accordion-border-color: var(--bs-border-color);--bs-accordion-border-width: var(--bs-border-width);--bs-accordion-border-radius: var(--bs-border-radius);--bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: var(--bs-body-color);--bs-accordion-btn-bg: var(--bs-accordion-bg);--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%231D1F21'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: 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.accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2366b0da'%3e%3cpath 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var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: 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reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #007bc2;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-tertiary-bg);--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid 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var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 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var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: .5rem;--bs-modal-color: ;--bs-modal-bg: var(--bs-body-bg);--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: var(--bs-border-width);--bs-modal-border-radius: var(--bs-border-radius-lg);--bs-modal-box-shadow: 0px 1px 2px RGBA(var(--bslib-box-shadow-color-rgb), 0.1),0px 2px 3px RGBA(var(--bslib-box-shadow-color-rgb), 0.1),0px 3px 8px RGBA(var(--bslib-box-shadow-color-rgb), 0.1);--bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1.5rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: none;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 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2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #464646;--bs-backdrop-opacity: .5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid 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.5rem;--bs-btn-font-size:.875rem;--bs-btn-border-radius: var(--bs-border-radius-sm)}.fade{transition:opacity 0.15s linear}@media (prefers-reduced-motion: reduce){.fade{transition:none}}.fade:not(.show){opacity:0}.collapse:not(.show){display:none}.collapsing{height:0;overflow:hidden;transition:height 0.35s ease}@media (prefers-reduced-motion: reduce){.collapsing{transition:none}}.collapsing.collapse-horizontal{width:0;height:auto;transition:width 0.35s ease}@media (prefers-reduced-motion: reduce){.collapsing.collapse-horizontal{transition:none}}.dropup,.dropend,.dropdown,.dropstart,.dropup-center,.dropdown-center{position:relative}.dropdown-toggle{white-space:nowrap}.dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:.3em solid;border-right:.3em solid transparent;border-bottom:0;border-left:.3em solid transparent}.dropdown-toggle:empty::after{margin-left:0}.dropdown-menu{--bs-dropdown-zindex: 1000;--bs-dropdown-min-width: 10rem;--bs-dropdown-padding-x: 0;--bs-dropdown-padding-y: .5rem;--bs-dropdown-spacer: .125rem;--bs-dropdown-font-size:1rem;--bs-dropdown-color: var(--bs-body-color);--bs-dropdown-bg: var(--bs-body-bg);--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-border-radius: var(--bs-border-radius);--bs-dropdown-border-width: var(--bs-border-width);--bs-dropdown-inner-border-radius: calc(var(--bs-border-radius) - var(--bs-border-width));--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-divider-margin-y: .5rem;--bs-dropdown-box-shadow: 0px 1px 2px RGBA(var(--bslib-box-shadow-color-rgb), 0.1),0px 3px 7px RGBA(var(--bslib-box-shadow-color-rgb), 0.1),0px 12px 30px RGBA(var(--bslib-box-shadow-color-rgb), 0.08);--bs-dropdown-link-color: var(--bs-body-color);--bs-dropdown-link-hover-color: var(--bs-body-color);--bs-dropdown-link-hover-bg: var(--bs-tertiary-bg);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #007bc2;--bs-dropdown-link-disabled-color: var(--bs-tertiary-color);--bs-dropdown-item-padding-x: 1rem;--bs-dropdown-item-padding-y: .25rem;--bs-dropdown-header-color: #707782;--bs-dropdown-header-padding-x: 1rem;--bs-dropdown-header-padding-y: .5rem;position:absolute;z-index:var(--bs-dropdown-zindex);display:none;min-width:var(--bs-dropdown-min-width);padding:var(--bs-dropdown-padding-y) var(--bs-dropdown-padding-x);margin:0;font-size:var(--bs-dropdown-font-size);color:var(--bs-dropdown-color);text-align:left;list-style:none;background-color:var(--bs-dropdown-bg);background-clip:padding-box;border:var(--bs-dropdown-border-width) solid var(--bs-dropdown-border-color);border-radius:var(--bs-dropdown-border-radius)}.dropdown-menu[data-bs-popper]{top:100%;left:0;margin-top:var(--bs-dropdown-spacer)}.dropdown-menu-start{--bs-position: start}.dropdown-menu-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-end{--bs-position: end}.dropdown-menu-end[data-bs-popper]{right:0;left:auto}@media (min-width: 576px){.dropdown-menu-sm-start{--bs-position: start}.dropdown-menu-sm-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-sm-end{--bs-position: end}.dropdown-menu-sm-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 768px){.dropdown-menu-md-start{--bs-position: start}.dropdown-menu-md-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-md-end{--bs-position: end}.dropdown-menu-md-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 992px){.dropdown-menu-lg-start{--bs-position: start}.dropdown-menu-lg-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-lg-end{--bs-position: end}.dropdown-menu-lg-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 1200px){.dropdown-menu-xl-start{--bs-position: start}.dropdown-menu-xl-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-xl-end{--bs-position: end}.dropdown-menu-xl-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 1400px){.dropdown-menu-xxl-start{--bs-position: start}.dropdown-menu-xxl-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-xxl-end{--bs-position: end}.dropdown-menu-xxl-end[data-bs-popper]{right:0;left:auto}}.dropup .dropdown-menu[data-bs-popper]{top:auto;bottom:100%;margin-top:0;margin-bottom:var(--bs-dropdown-spacer)}.dropup .dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:0;border-right:.3em solid transparent;border-bottom:.3em solid;border-left:.3em solid transparent}.dropup .dropdown-toggle:empty::after{margin-left:0}.dropend .dropdown-menu[data-bs-popper]{top:0;right:auto;left:100%;margin-top:0;margin-left:var(--bs-dropdown-spacer)}.dropend .dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:.3em solid transparent;border-right:0;border-bottom:.3em solid transparent;border-left:.3em solid}.dropend .dropdown-toggle:empty::after{margin-left:0}.dropend .dropdown-toggle::after{vertical-align:0}.dropstart .dropdown-menu[data-bs-popper]{top:0;right:100%;left:auto;margin-top:0;margin-right:var(--bs-dropdown-spacer)}.dropstart .dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:""}.dropstart .dropdown-toggle::after{display:none}.dropstart .dropdown-toggle::before{display:inline-block;margin-right:.255em;vertical-align:.255em;content:"";border-top:.3em solid transparent;border-right:.3em solid;border-bottom:.3em solid transparent}.dropstart .dropdown-toggle:empty::after{margin-left:0}.dropstart .dropdown-toggle::before{vertical-align:0}.dropdown-divider{height:0;margin:var(--bs-dropdown-divider-margin-y) 0;overflow:hidden;border-top:1px solid var(--bs-dropdown-divider-bg);opacity:1}.dropdown-item{display:block;width:100%;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);clear:both;font-weight:400;color:var(--bs-dropdown-link-color);text-align:inherit;text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;white-space:nowrap;background-color:transparent;border:0;border-radius:var(--bs-dropdown-item-border-radius, 0)}.dropdown-item:hover,.dropdown-item:focus{color:var(--bs-dropdown-link-hover-color);background-color:var(--bs-dropdown-link-hover-bg)}.dropdown-item.active,.dropdown-item:active{color:var(--bs-dropdown-link-active-color);text-decoration:none;background-color:var(--bs-dropdown-link-active-bg)}.dropdown-item.disabled,.dropdown-item:disabled{color:var(--bs-dropdown-link-disabled-color);pointer-events:none;background-color:transparent}.dropdown-menu.show{display:block}.dropdown-header{display:block;padding:var(--bs-dropdown-header-padding-y) var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text{display:block;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);color:var(--bs-dropdown-link-color)}.dropdown-menu-dark{--bs-dropdown-color: #dee2e6;--bs-dropdown-bg: #343A46;--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-box-shadow: ;--bs-dropdown-link-color: #dee2e6;--bs-dropdown-link-hover-color: #fff;--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-link-hover-bg: rgba(255,255,255,0.15);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #007bc2;--bs-dropdown-link-disabled-color: #8D959E;--bs-dropdown-header-color: #8D959E}.btn-group,.btn-group-vertical{position:relative;display:inline-flex;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:var(--bs-border-radius)}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(var(--bs-border-width) * -1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n + 3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:1.5em;padding-left:1.5em}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(var(--bs-border-width) * -1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-link-color);--bs-nav-link-hover-color: var(--bs-link-hover-color);--bs-nav-link-disabled-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem rgba(0,123,194,0.25)}.nav-link.disabled,.nav-link:disabled{color:var(--bs-nav-link-disabled-color);pointer-events:none;cursor:default}.nav-tabs{--bs-nav-tabs-border-width: var(--bs-border-width);--bs-nav-tabs-border-color: var(--bs-border-color);--bs-nav-tabs-border-radius: var(--bs-border-radius);--bs-nav-tabs-link-hover-border-color: var(--bs-secondary-bg) var(--bs-secondary-bg) var(--bs-border-color);--bs-nav-tabs-link-active-color: var(--bs-emphasis-color);--bs-nav-tabs-link-active-bg: var(--bs-body-bg);--bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1 * var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid transparent;border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * var(--bs-nav-tabs-border-width));border-top-left-radius:0;border-top-right-radius:0}.nav-pills{--bs-nav-pills-border-radius: var(--bs-border-radius);--bs-nav-pills-link-active-color: #fff;--bs-nav-pills-link-active-bg: #007bc2}.nav-pills .nav-link{border-radius:var(--bs-nav-pills-border-radius)}.nav-pills .nav-link.active,.nav-pills .show>.nav-link{color:var(--bs-nav-pills-link-active-color);background-color:var(--bs-nav-pills-link-active-bg)}.nav-underline{--bs-nav-underline-gap: 1rem;--bs-nav-underline-border-width: .125rem;--bs-nav-underline-link-active-color: var(--bs-emphasis-color);gap:var(--bs-nav-underline-gap)}.nav-underline .nav-link{padding-right:0;padding-left:0;border-bottom:var(--bs-nav-underline-border-width) solid transparent}.nav-underline .nav-link:hover,.nav-underline .nav-link:focus{border-bottom-color:currentcolor}.nav-underline .nav-link.active,.nav-underline .show>.nav-link{font-weight:700;color:var(--bs-nav-underline-link-active-color);border-bottom-color:currentcolor}.nav-fill>.nav-link,.nav-fill .nav-item{flex:1 1 auto;-webkit-flex:1 1 auto;text-align:center}.nav-justified>.nav-link,.nav-justified .nav-item{flex-basis:0;-webkit-flex-basis:0;flex-grow:1;-webkit-flex-grow:1;text-align:center}.nav-fill .nav-item .nav-link,.nav-justified .nav-item .nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x: .66rem;--bs-navbar-padding-y: .5rem;--bs-navbar-color: rgba(var(--bs-emphasis-color-rgb), 0.65);--bs-navbar-hover-color: rgba(var(--bs-emphasis-color-rgb), 0.8);--bs-navbar-disabled-color: rgba(var(--bs-emphasis-color-rgb), 0.3);--bs-navbar-active-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-padding-y: .3125rem;--bs-navbar-brand-margin-end: 1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-hover-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-nav-link-padding-x: .5rem;--bs-navbar-toggler-padding-y: .25rem;--bs-navbar-toggler-padding-x: .75rem;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%2829,31,33,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(var(--bs-emphasis-color-rgb), 0.15);--bs-navbar-toggler-border-radius: var(--bs-border-radius);--bs-navbar-toggler-focus-width: .25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) 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.navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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!important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme="dark"]{--bs-navbar-color: rgba(var(--bs-emphasis-color-rgb), 0.55);--bs-navbar-hover-color: rgba(var(--bs-emphasis-color-rgb), 0.75);--bs-navbar-disabled-color: rgba(var(--bs-emphasis-color-rgb), 0.25);--bs-navbar-active-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-hover-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-toggler-border-color: rgba(var(--bs-emphasis-color-rgb), 0.1);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme="dark"] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: .5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: var(--bs-border-width);--bs-card-border-color: var(--bs-border-color-translucent);--bs-card-border-radius: 8px;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(8px - (var(--bs-border-width)));--bs-card-cap-padding-y: .5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: var(--bs-body-bg);--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: .75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color);border-radius:var(--bs-card-border-radius)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0;border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card>.list-group:last-child{border-bottom-width:0;border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-.5 * 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wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: var(--bs-body-color);--bs-accordion-bg: var(--bs-body-bg);--bs-accordion-transition: color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out,border-radius 0.15s ease;--bs-accordion-border-color: var(--bs-border-color);--bs-accordion-border-width: var(--bs-border-width);--bs-accordion-border-radius: var(--bs-border-radius);--bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: var(--bs-body-color);--bs-accordion-btn-bg: var(--bs-accordion-bg);--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%231D1F21'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2300314e'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #80bde1;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(0,123,194,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: ;--bs-accordion-active-bg: }.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media (prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1 * var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media (prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2366b0da'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2366b0da'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: var(--bs-secondary-color);--bs-breadcrumb-item-padding-x: .5rem;--bs-breadcrumb-item-active-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(0,123,194,0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #007bc2;--bs-pagination-active-border-color: #007bc2;--bs-pagination-disabled-color: var(--bs-secondary-color);--bs-pagination-disabled-bg: var(--bs-secondary-bg);--bs-pagination-disabled-border-color: var(--bs-border-color);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #007bc2;--bs-progress-bar-transition: width 0.6s 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var(--bs-body-bg);--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #007bc2;--bs-list-group-active-border-color: #007bc2;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") 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var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * 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576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 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992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 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var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: 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var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: .5;--bs-btn-close-hover-opacity: .75;--bs-btn-close-focus-shadow: 0 0 0 .25rem rgba(0,123,194,0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: .25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:transparent var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:3px;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme="dark"] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: .75rem;--bs-toast-padding-y: .5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-border-width: var(--bs-border-width);--bs-toast-border-color: var(--bs-border-color-translucent);--bs-toast-border-radius: var(--bs-border-radius);--bs-toast-box-shadow: var(--bs-box-shadow);--bs-toast-header-color: var(--bs-secondary-color);--bs-toast-header-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-header-border-color: var(--bs-border-color-translucent);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-.5 * 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diff --git a/nexus/index.html b/nexus/index.html
index 1df42459b..58bf9b784 100644
--- a/nexus/index.html
+++ b/nexus/index.html
@@ -238,6 +238,7 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
   </main><aside class="col-md-3"><div class="links">
 <h2 data-toc-skip>Links</h2>
 <ul class="list-unstyled">
+<li><a href="https://cloud.r-project.org/package=nexus" class="external-link">View on CRAN</a></li>
 <li><a href="https://github.com/tesselle/nexus/" class="external-link">Browse source code</a></li>
 <li><a href="https://github.com/tesselle/nexus/issues" class="external-link">Report a bug</a></li>
 </ul>
diff --git a/nexus/news/index.html b/nexus/news/index.html
index 5174e9f62..11dac8dff 100644
--- a/nexus/news/index.html
+++ b/nexus/news/index.html
@@ -54,7 +54,7 @@
     </div>
 
     <div class="section level2">
-<h2 class="pkg-version" data-toc-text="0.1.0" id="nexus-010">nexus 0.1.0<a class="anchor" aria-label="anchor" href="#nexus-010"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.1.0" id="nexus-010">nexus 0.1.0<a class="anchor" aria-label="anchor" href="#nexus-010"></a></h2><p class="text-muted">CRAN release: 2023-11-29</p>
 <ul><li>First release.</li>
 </ul></div>
   </main></div>
diff --git a/nexus/pkgdown.yml b/nexus/pkgdown.yml
index d2cea18c5..614b7d39d 100644
--- a/nexus/pkgdown.yml
+++ b/nexus/pkgdown.yml
@@ -4,7 +4,7 @@ pkgdown_sha: ~
 articles:
   bibliography: bibliography.html
   nexus: nexus.html
-last_built: 2023-11-29T08:33Z
+last_built: 2023-11-30T10:13Z
 urls:
   reference: https://packages.tesselle.org/nexus/reference
   article: https://packages.tesselle.org/nexus/articles
diff --git a/nexus/reference/Rplot003.png b/nexus/reference/Rplot003.png
index f8a1e82978b4478e17e421a01af1ba619517dc72..fe055f6f6fc5c6ca3a80368f7506505a72a4de94 100644
GIT binary patch
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UgX7w(l@bxo2ah<y$>59s1l){DegFUf

diff --git a/nexus/reference/as_amounts.html b/nexus/reference/as_amounts.html
index 840e14f2d..7d30f190e 100644
--- a/nexus/reference/as_amounts.html
+++ b/nexus/reference/as_amounts.html
@@ -105,8 +105,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Row sums are internally stored before coercing to relative frequencies</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="totals.html">get_totals</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 202 285 198 262 325 277 141 243 338 197 247 192 261 345 271 201 124 182 210</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [20] 411</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 245 201 256 241 152 177 438 248 237 356 317 190 261 265 255 277 301 273 251</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [20] 278</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## This allows to restore the source data</span></span></span>
 <span class="r-in"><span><span class="va">A2</span> <span class="op">&lt;-</span> <span class="fu">as_amounts</span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
@@ -114,13 +114,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co">## Coerce to a data.frame</span></span></span>
 <span class="r-in"><span><span class="va">X</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">X</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           V1         V2        V3         V4         V5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S1 0.4306931 0.13366337 0.1930693 0.00990099 0.23267327</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S2 0.3438596 0.06666667 0.1578947 0.09473684 0.33684211</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S3 0.2525253 0.15151515 0.2373737 0.24242424 0.11616162</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S4 0.1603053 0.15267176 0.2633588 0.16030534 0.26335878</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S5 0.2707692 0.22461538 0.3015385 0.14153846 0.06153846</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S6 0.2563177 0.33212996 0.1191336 0.05054152 0.24187726</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>             V1         V2         V3        V4        V5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S1 0.289795918 0.07346939 0.17142857 0.2204082 0.2448980</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S2 0.243781095 0.16417910 0.06467662 0.2985075 0.2288557</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S3 0.210937500 0.17187500 0.10546875 0.3554688 0.1562500</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S4 0.004149378 0.26970954 0.29460581 0.2655602 0.1659751</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S5 0.105263158 0.32894737 0.17763158 0.1710526 0.2171053</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S6 0.028248588 0.16384181 0.32203390 0.2824859 0.2033898</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/nexus/reference/as_composition.html b/nexus/reference/as_composition.html
index 3a304fcec..593d28146 100644
--- a/nexus/reference/as_composition.html
+++ b/nexus/reference/as_composition.html
@@ -158,8 +158,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Row sums are internally stored before coercing to relative frequencies</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="totals.html">get_totals</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 236 212 297 289 262 285 286 139 256 227 212 303 294 230 299 250 239 169 377</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [20] 256</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 167 276 214 286 273 251 293 234 285 324 323 311 259 233 249 232 220 223 177</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [20] 323</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## This allows to restore the source data</span></span></span>
 <span class="r-in"><span><span class="va">A2</span> <span class="op">&lt;-</span> <span class="fu"><a href="as_amounts.html">as_amounts</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
@@ -167,13 +167,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co">## Coerce to a data.frame</span></span></span>
 <span class="r-in"><span><span class="va">X</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">X</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            V1        V2          V3          V4        V5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S1 0.11864407 0.1652542 0.296610169 0.008474576 0.4110169</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S2 0.01415094 0.4056604 0.009433962 0.301886792 0.2688679</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S3 0.19528620 0.3131313 0.279461279 0.080808081 0.1313131</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S4 0.07612457 0.1764706 0.259515571 0.269896194 0.2179931</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S5 0.09923664 0.2175573 0.221374046 0.106870229 0.3549618</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S6 0.32982456 0.2210526 0.007017544 0.333333333 0.1087719</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>           V1        V2          V3         V4         V5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S1 0.4610778 0.1736527 0.077844311 0.06586826 0.22155689</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S2 0.3586957 0.1123188 0.003623188 0.35507246 0.17028986</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S3 0.1121495 0.2336449 0.280373832 0.35981308 0.01401869</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S4 0.2482517 0.1293706 0.290209790 0.27622378 0.05594406</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S5 0.1062271 0.1978022 0.124542125 0.20879121 0.36263736</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S6 0.2509960 0.3426295 0.103585657 0.08366534 0.21912351</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/nexus/reference/as_features.html b/nexus/reference/as_features.html
index 11b916a23..2102aca4e 100644
--- a/nexus/reference/as_features.html
+++ b/nexus/reference/as_features.html
@@ -103,8 +103,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Row sums are internally stored before coercing to relative frequencies</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="totals.html">get_totals</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 247 183 234 286 288 323 232 286 221 346 239 298 323 219 172 127 238 171 272</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [20] 261</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 337 321 154 216 134 315 328 188 373 227 248 231 184 108 368 271 254 273 352</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [20] 130</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## This allows to restore the source data</span></span></span>
 <span class="r-in"><span><span class="va">A2</span> <span class="op">&lt;-</span> <span class="fu"><a href="as_amounts.html">as_amounts</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
@@ -112,13 +112,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co">## Coerce to a data.frame</span></span></span>
 <span class="r-in"><span><span class="va">X</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">X</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           V1         V2        V3         V4         V5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S1 0.2145749 0.37651822 0.1052632 0.14170040 0.16194332</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S2 0.2896175 0.22950820 0.1092896 0.16393443 0.20765027</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S3 0.4230769 0.14529915 0.2692308 0.01709402 0.14529915</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S4 0.1258741 0.16783217 0.2517483 0.25524476 0.19930070</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S5 0.2465278 0.15277778 0.2604167 0.32638889 0.01388889</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S6 0.2569659 0.09597523 0.2043344 0.18885449 0.25386997</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>           V1         V2         V3         V4         V5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S1 0.2759644 0.08902077 0.11572700 0.23145401 0.28783383</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S2 0.3084112 0.13707165 0.29283489 0.01246106 0.24922118</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S3 0.1428571 0.22727273 0.14285714 0.44155844 0.04545455</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S4 0.0462963 0.28240741 0.45370370 0.09259259 0.12500000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S5 0.1791045 0.20895522 0.01492537 0.56716418 0.02985075</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S6 0.2793651 0.29206349 0.25714286 0.09841270 0.07301587</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/nexus/reference/mahalanobis.html b/nexus/reference/mahalanobis.html
index d7b040e24..7797d1d44 100644
--- a/nexus/reference/mahalanobis.html
+++ b/nexus/reference/mahalanobis.html
@@ -135,13 +135,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">mahalanobis</span><span class="op">(</span><span class="va">coda</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>         H1         H2         H3         H4         H5         H6         H7 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  4.4350697  0.5293921  2.2463414  1.3647605 13.0184551  5.8324688 18.0231947 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  5.1307933  0.5074407  2.1126269  2.8998211 19.0843749  5.6475980 33.6658536 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>         H8         H9        H10        H11        H12        H13        H14 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25.4910136  1.4264228 18.0221709  2.0490574  5.4416081  5.0114990  6.3602144 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 25.7137516  1.2502063 56.7461076  1.7542646  5.9925765  4.5827167 22.1196584 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>        H15        H16        H17        H18        H19        H20        H21 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.9635308  6.4062999  7.3866770  1.5149231  2.9208378  4.7044107  1.1393424 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.9526275 25.7147694  7.6312860  2.1336759  2.5354194  4.4406867  2.3685685 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>        H22        H23        H24        H25 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6158258 10.0169365  2.7827184  4.8516637 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3.7668943 25.9902036  6.7638903  6.5289073 </span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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index 534200626..e24fd183c 100644
--- a/nexus/reference/totals.html
+++ b/nexus/reference/totals.html
@@ -112,8 +112,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Row sums are internally stored before coercing to relative frequencies</span></span></span>
 <span class="r-in"><span><span class="fu">get_totals</span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 232 341 140 199 252 296 155 263 253 268 249 188 252 286 278 172 304 241 237</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [20] 221</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 297 353 234 406 214 199 222  93 237 203 313 304 216 303 266 255 237 116 175</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [20] 350</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## This allows to restore the source data</span></span></span>
 <span class="r-in"><span><span class="va">A2</span> <span class="op">&lt;-</span> <span class="fu"><a href="as_amounts.html">as_amounts</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
@@ -121,13 +121,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co">## Coerce to a data.frame</span></span></span>
 <span class="r-in"><span><span class="va">X</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="va">B</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">X</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            V1         V2         V3        V4         V5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S1 0.07758621 0.01724138 0.40086207 0.1206897 0.38362069</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S2 0.27859238 0.10557185 0.25219941 0.1612903 0.20234604</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S3 0.33571429 0.26428571 0.02142857 0.3642857 0.01428571</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S4 0.22110553 0.30150754 0.10552764 0.1206030 0.25125628</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S5 0.32936508 0.13095238 0.11904762 0.1428571 0.27777778</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> S6 0.13851351 0.19932432 0.11824324 0.2162162 0.32770270</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>           V1         V2         V3        V4         V5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S1 0.3097643 0.31313131 0.01683502 0.1515152 0.20875421</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S2 0.2662890 0.21246459 0.27762040 0.1444759 0.09915014</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S3 0.3418803 0.05555556 0.05128205 0.3162393 0.23504274</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S4 0.2216749 0.22906404 0.11330049 0.2413793 0.19458128</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S5 0.2897196 0.10280374 0.26635514 0.2570093 0.08411215</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> S6 0.2462312 0.07537688 0.48241206 0.1859296 0.01005025</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/nexus/search.json b/nexus/search.json
index 3ab10ea1a..7086b6613 100644
--- a/nexus/search.json
+++ b/nexus/search.json
@@ -1 +1 @@
-[{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"get-started","dir":"Articles","previous_headings":"","what":"Get started","title":"Introduction to nexus","text":"nexus provides set S4 classes represent different special types matrix. basic class represents compositional data matrix, .e. quantitative (positive) descriptions parts whole, carrying relative, rather absolute, information (Aitchison 1986; Greenacre 2021). assumes keep data tidy: variable must saved column observation (sample) must saved row. class simple use inherits base matrix: CompositionMatrix represents closed composition matrix: row matrix sum 1 (relative changes relevant compositional data analysis). original row sums kept internally, source data can restored:","code":"## Install extra packages (if needed): # install.packages(\"folio\")  library(nexus) ## Mineral compositions of rock specimens ## Data from Aitchison 1986 data(\"hongite\") head(hongite) #>       A    B    C    D    E #> H1 48.8 31.7  3.8  6.4  9.3 #> H2 48.2 23.8  9.0  9.2  9.8 #> H3 37.0  9.1 34.2  9.5 10.2 #> H4 50.9 23.8  7.2 10.1  8.0 #> H5 44.2 38.3  2.9  7.7  6.9 #> H6 52.3 26.2  4.2 12.5  4.8  ## Coerce to compositional data coda <- as_composition(hongite) head(coda) #> <CompositionMatrix: 6 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093 #> H2 0.482 0.238 0.090 0.092 0.098 #> H3 0.370 0.091 0.342 0.095 0.102 #> H4 0.509 0.238 0.072 0.101 0.080 #> H5 0.442 0.383 0.029 0.077 0.069 #> H6 0.523 0.262 0.042 0.125 0.048 ## Coerce to count data counts <- as_amounts(coda)  all.equal(hongite, as.data.frame(counts)) #> [1] TRUE ## Ternary plots plot(coda) ## Compositional bar plot barplot(coda, order = \"A\")"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"working-with-reference-groups","dir":"Articles","previous_headings":"Get started","what":"Working with (reference) groups","title":"Introduction to nexus","text":"Provenance studies typically rely two approaches, can used together: Identification groups among artifacts studied, based mineralogical geochemical criteria (clustering). Comparison -called reference groups, .e. known geological sources productive contexts (classification). nexus allows specify whether observation belongs specific group (). coercing data.frame CompositionMatrix object, attempt made automatically detect groups mapping column names. behavior can disabled setting options(nexus.autodetect = FALSE) overridden explicitly specifying column used groups argument as_composition(). get_groups(x) set_groups(x) <- value allow retrieve set groups existing CompositionMatrix (NA can used specify sample belong group): groups defined, can used methods (e.g. plotting).","code":"## Create a data.frame X <- data.frame(   groups = c(\"A\", \"A\", \"B\", \"A\", \"B\", \"C\", \"C\", \"C\", \"B\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X) any_assigned(Y) #> [1] TRUE ## Set groups (NA means no group) set_groups(Y) <- c(\"X\", \"X\", \"Y\", \"X\", \"Y\", NA, NA, NA, \"Y\")  ## Retrieve groups get_groups(Y) #> [1] \"X\" \"X\" \"Y\" \"X\" \"Y\" NA  NA  NA  \"Y\""},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"working-with-repeated-measurements","dir":"Articles","previous_headings":"Get started","what":"Working with repeated measurements","title":"Introduction to nexus","text":"situations, measurements may repeated (e.g. multiple chemical analyses sample). presence repeated measurements can specified giving several observations sample name. coercing data.frame CompositionMatrix object, attempt made automatically detect samples mapping column names. matching column found, row names used default. behavior can disabled setting options(nexus.autodetect = FALSE) overridden explicitly specifying column used samples argument as_composition(). get_samples(x) set_samples(x) <- value allow retrieve set sample names existing CompositionMatrix (missing values allowed): Note presence repeated measurements may affect calculations (read documentation carefully).","code":"## Create a data.frame X <- data.frame(   samples = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"C\", \"C\", \"C\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X) any_replicated(Y) #> [1] TRUE ## Set sample names set_samples(Y) <- c(\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\")  ## Retrieve groups get_samples(Y) #> [1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\" \"G\" \"H\" \"I\""},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"log-ratio-transformations","dir":"Articles","previous_headings":"","what":"Log-ratio transformations","title":"Introduction to nexus","text":"package provides following transformations: centered log ratio (CLR, Aitchison 1986), additive log ratio (ALR, Aitchison 1986), isometric log ratio (ILR, Egozcue et al. 2003) pivot log-ratio (PLR, Hron et al. 2017).","code":"## CLR clr <- transform_clr(coda) head(clr) #>            A          B          C           D          E #> H1 1.3346582  0.9032446 -1.2180710 -0.69677410 -0.3230577 #> H2 1.1265880  0.4209146 -0.5515464 -0.52956753 -0.4663886 #> H3 0.8258984 -0.5767451  0.7472061 -0.53372770 -0.4626318 #> H4 1.2367416  0.4765643 -0.7190403 -0.38058588 -0.6136798 #> H5 1.2943994  1.1511245 -1.4296147 -0.45310510 -0.5628040 #> H6 1.4065594  0.7153224 -1.1153524 -0.02470833 -0.9818211  plot(clr) back <- transform_inverse(clr) head(back) #> <CompositionMatrix: 6 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093 #> H2 0.482 0.238 0.090 0.092 0.098 #> H3 0.370 0.091 0.342 0.095 0.102 #> H4 0.509 0.238 0.072 0.101 0.080 #> H5 0.442 0.383 0.029 0.077 0.069 #> H6 0.523 0.262 0.042 0.125 0.048"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"multivariate-methods","dir":"Articles","previous_headings":"","what":"Multivariate methods","title":"Introduction to nexus","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\")  ## Remove rows with missing values kommos <- remove_NA(kommos, margin = 1)  ## Coerce to a compositional matrix coda <- as_composition(kommos) #> 2 qualitative variables were removed: type, date.  ## Set groups set_groups(coda) <- kommos$type ## Compositional bar plot barplot(coda, order = \"Ca\")"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"principle-component-analysis","dir":"Articles","previous_headings":"Multivariate methods","what":"Principle Component Analysis","title":"Introduction to nexus","text":"","code":"## CLR clr <- transform_clr(coda)  ## PCA library(dimensio)  clr_pca <- pca(clr, scale = FALSE)  viz_individuals(clr_pca, highlight = get_groups(coda), pch = 16,                  col = c(\"#EE7733\", \"#0077BB\", \"#33BBEE\", \"#EE3377\")) viz_variables(clr_pca)"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"manova","dir":"Articles","previous_headings":"Multivariate methods","what":"MANOVA","title":"Introduction to nexus","text":"MANOVA results suggest statistically significant differences groups.","code":"## ILR ilr <- transform_ilr(coda)  ## MANOVA fit <- manova(ilr ~ get_groups(ilr)) summary(fit) #>                 Df Pillai approx F num Df den Df    Pr(>F)     #> get_groups(ilr)  3 1.7932   4.8487     57    186 < 2.2e-16 *** #> Residuals       78                                             #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"discriminant-analysis","dir":"Articles","previous_headings":"Multivariate methods","what":"Discriminant Analysis","title":"Introduction to nexus","text":"","code":"## LDA discr <- MASS::lda(ilr, grouping = get_groups(ilr)) plot(discr) ## Back transform results transform_inverse(discr$means, origin = ilr) #>              Sm           Lu           U          Yb         As           Sb #> CJ  0.014938709 0.0008558484 0.007253021 0.006793156 0.02199766 0.0014569243 #> EJ  0.018613143 0.0010139916 0.007584757 0.008127263 0.01819848 0.0012892835 #> SNA 0.007141080 0.0005084305 0.003701554 0.003950209 0.01127728 0.0009184591 #> TSJ 0.009835545 0.0006601008 0.005386968 0.005152492 0.01369280 0.0018069781 #>              Ca           Na         La         Ce         Th        Cr #> CJ  0.029204741 0.0009599806 0.07678692 0.16797505 0.01897389 0.4392215 #> EJ  0.025610707 0.0018649908 0.09678585 0.21657222 0.02321610 0.3286455 #> SNA 0.007737157 0.0012335187 0.03893221 0.08823033 0.01467079 0.6248961 #> TSJ 0.008745270 0.0013825884 0.05646163 0.11939198 0.01937137 0.4647827 #>              Hf          Cs         Sc         Rb          Fe          Ta #> CJ  0.011779918 0.004874522 0.03227958 0.10114053 0.010336253 0.003030762 #> EJ  0.017996210 0.004785199 0.04344994 0.10870167 0.013130417 0.004482816 #> SNA 0.005908416 0.004618774 0.03019832 0.09111503 0.008644199 0.001635972 #> TSJ 0.008497490 0.011135602 0.03551225 0.17345781 0.010099512 0.002099366 #>             Co          Eu #> CJ  0.04679035 0.003350694 #> EJ  0.05566413 0.004267303 #> SNA 0.05301923 0.001662958 #> TSJ 0.05036299 0.002164612"},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/nexus/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Sourcing Archaeological Materials by Chemical Composition","text":"Exploration analysis compositional data framework Aitchison (1986). nexus provides tools chemical fingerprinting source tracking ancient materials. package provides methods compositional data analysis: Logratio transformations: transform_lr(), transform_clr(), transform_alr(), transform_ilr(), transform_plr(). Compositional statistics. Zero missing value replacement. Outlier detection: outliers(). package also includes methods provenance studies: Multivariate analysis: pca(). Mixed-mode analysis using geochemical petrographic data (Baxter et al. 2008): mix(). isopleuros companion package nexus allows create ternary plots.","code":"To cite nexus in publications use:    Frerebeau N, Philippe A (2023). _nexus: Sourcing Archaeological   Materials by Chemical Composition_. Université Bordeaux Montaigne,   Pessac, France. R package version 0.1.0,   <https://packages.tesselle.org/nexus/>.  A BibTeX entry for LaTeX users is    @Manual{,     author = {Nicolas Frerebeau and Anne Philippe},     title = {{nexus: Sourcing Archaeological Materials by Chemical Composition}},     year = {2023},     organization = {Université Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 0.1.0},     url = {https://packages.tesselle.org/nexus/},   }  This package is a part of the tesselle project <https://www.tesselle.org>."},{"path":"https://packages.tesselle.org/nexus/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Sourcing Archaeological Materials by Chemical Composition","text":"can install released version nexus CRAN : development version GitHub :","code":"install.packages(\"nexus\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/nexus\")"},{"path":"https://packages.tesselle.org/nexus/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Sourcing Archaeological Materials by Chemical Composition","text":"nexus provides set S4 classes represent different special types matrix. basic class represents compositional data matrix, .e. quantitative (nonnegative) descriptions parts whole, carrying relative, rather absolute, information (Aitchison 1986). assumes keep data tidy: variable must saved column observation (sample) must saved row. new classes simple use inherit base matrix (see vignette(\"nexus\")): nexus allows specify whether observation belongs specific group (). Additionally, presence repeated measurements can specified giving several observations sample name:","code":"## Load the package library(nexus) ## Mineral compositions of rock specimens ## Data from Aitchison 1986 data(\"hongite\") head(hongite) #>       A    B    C    D    E #> H1 48.8 31.7  3.8  6.4  9.3 #> H2 48.2 23.8  9.0  9.2  9.8 #> H3 37.0  9.1 34.2  9.5 10.2 #> H4 50.9 23.8  7.2 10.1  8.0 #> H5 44.2 38.3  2.9  7.7  6.9 #> H6 52.3 26.2  4.2 12.5  4.8  ## Coerce to compositional data coda <- as_composition(hongite) head(coda) #> <CompositionMatrix: 6 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093 #> H2 0.482 0.238 0.090 0.092 0.098 #> H3 0.370 0.091 0.342 0.095 0.102 #> H4 0.509 0.238 0.072 0.101 0.080 #> H5 0.442 0.383 0.029 0.077 0.069 #> H6 0.523 0.262 0.042 0.125 0.048 ## Mineral compositions of five slides as reported by five analysts ## Data from Aitchison 1986 data(\"slides\") head(slides) #>   analyst slide quartz microcline plagioclass biotite muscovite opaques #> 1      A1     A   24.7       35.6        33.3     3.3       2.0     0.6 #> 2      A1     B   26.8       35.7        32.6     3.5       0.4     0.6 #> 3      A1     C   28.0       34.2        32.1     3.4       1.1     0.7 #> 4      A1     D   27.8       35.0        31.5     3.3       1.0     0.9 #> 5      A1     E   26.6       34.5        33.6     3.0       1.4     0.6 #> 6      A2     A   27.3       35.5        32.1     2.5       1.5     0.8 #>   nonopaques #> 1        0.6 #> 2        0.4 #> 3        0.4 #> 4        0.5 #> 5        0.3 #> 6        0.3  ## Coerce to compositional data coda <- as_composition(slides, sample = 2, group = 1) head(coda) #> <CompositionMatrix: 6 x 7> #>      quartz microcline plagioclass    biotite  muscovite     opaques #> 1 0.2467532  0.3556444   0.3326673 0.03296703 0.01998002 0.005994006 #> 2 0.2680000  0.3570000   0.3260000 0.03500000 0.00400000 0.006000000 #> 3 0.2802803  0.3423423   0.3213213 0.03403403 0.01101101 0.007007007 #> 4 0.2780000  0.3500000   0.3150000 0.03300000 0.01000000 0.009000000 #> 5 0.2660000  0.3450000   0.3360000 0.03000000 0.01400000 0.006000000 #> 6 0.2730000  0.3550000   0.3210000 0.02500000 0.01500000 0.008000000 #>    nonopaques #> 1 0.005994006 #> 2 0.004000000 #> 3 0.004004004 #> 4 0.005000000 #> 5 0.003000000 #> 6 0.003000000"},{"path":"https://packages.tesselle.org/nexus/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Sourcing Archaeological Materials by Chemical Composition","text":"Please note nexus project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Compositional Matrix — CompositionMatrix-class","title":"Compositional Matrix — CompositionMatrix-class","text":"S4 class represent compositional data.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Compositional Matrix — CompositionMatrix-class","text":"totals numeric vector store absolute row sums (closure compositions). codes character vector store laboratory codes (unique identifiers). samples character vector store sample identifiers (allows duplicates case repeated measurements). groups character vector store group names ().","code":""},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Compositional Matrix — CompositionMatrix-class","text":"class inherits matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Compositional Matrix — CompositionMatrix-class","text":"code snippets , x CompositionMatrix object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compositional Matrix — CompositionMatrix-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compositional Matrix — CompositionMatrix-class","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## codaccess dim(coda) # Get the matrix dimensions #> [1] 25  5 row(coda) # Get the row indexes #>       [,1] [,2] [,3] [,4] [,5] #>  [1,]    1    1    1    1    1 #>  [2,]    2    2    2    2    2 #>  [3,]    3    3    3    3    3 #>  [4,]    4    4    4    4    4 #>  [5,]    5    5    5    5    5 #>  [6,]    6    6    6    6    6 #>  [7,]    7    7    7    7    7 #>  [8,]    8    8    8    8    8 #>  [9,]    9    9    9    9    9 #> [10,]   10   10   10   10   10 #> [11,]   11   11   11   11   11 #> [12,]   12   12   12   12   12 #> [13,]   13   13   13   13   13 #> [14,]   14   14   14   14   14 #> [15,]   15   15   15   15   15 #> [16,]   16   16   16   16   16 #> [17,]   17   17   17   17   17 #> [18,]   18   18   18   18   18 #> [19,]   19   19   19   19   19 #> [20,]   20   20   20   20   20 #> [21,]   21   21   21   21   21 #> [22,]   22   22   22   22   22 #> [23,]   23   23   23   23   23 #> [24,]   24   24   24   24   24 #> [25,]   25   25   25   25   25 col(coda, as.factor = TRUE) # Get the column indexes #>       [,1] [,2] [,3] [,4] [,5] #>  [1,] A    B    C    D    E    #>  [2,] A    B    C    D    E    #>  [3,] A    B    C    D    E    #>  [4,] A    B    C    D    E    #>  [5,] A    B    C    D    E    #>  [6,] A    B    C    D    E    #>  [7,] A    B    C    D    E    #>  [8,] A    B    C    D    E    #>  [9,] A    B    C    D    E    #> [10,] A    B    C    D    E    #> [11,] A    B    C    D    E    #> [12,] A    B    C    D    E    #> [13,] A    B    C    D    E    #> [14,] A    B    C    D    E    #> [15,] A    B    C    D    E    #> [16,] A    B    C    D    E    #> [17,] A    B    C    D    E    #> [18,] A    B    C    D    E    #> [19,] A    B    C    D    E    #> [20,] A    B    C    D    E    #> [21,] A    B    C    D    E    #> [22,] A    B    C    D    E    #> [23,] A    B    C    D    E    #> [24,] A    B    C    D    E    #> [25,] A    B    C    D    E    #> Levels: A B C D E nrow(coda) # Get the number of rows #> [1] 25 ncol(coda) # Get the number of columns #> [1] 5 dimnames(coda) # Get the dimension names #> [[1]] #>  [1] \"H1\"  \"H2\"  \"H3\"  \"H4\"  \"H5\"  \"H6\"  \"H7\"  \"H8\"  \"H9\"  \"H10\" \"H11\" \"H12\" #> [13] \"H13\" \"H14\" \"H15\" \"H16\" \"H17\" \"H18\" \"H19\" \"H20\" \"H21\" \"H22\" \"H23\" \"H24\" #> [25] \"H25\" #>  #> [[2]] #> [1] \"A\" \"B\" \"C\" \"D\" \"E\" #>  rownames(coda) <- LETTERS[1:25] # Set the row names rownames(coda) # Get the rownames #>  [1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\" \"G\" \"H\" \"I\" \"J\" \"K\" \"L\" \"M\" \"N\" \"O\" \"P\" \"Q\" \"R\" \"S\" #> [20] \"T\" \"U\" \"V\" \"W\" \"X\" \"Y\" colnames(coda) <- letters[21:25] # Set the column names colnames(coda) # Get the column names #> [1] \"u\" \"v\" \"w\" \"x\" \"y\""},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Log-Ratio Matrix — LogRatio-class","title":"Log-Ratio Matrix — LogRatio-class","text":"S4 classes represent log-ratio data transformations.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Log-Ratio Matrix — LogRatio-class","text":"totals numeric vector store absolute row sums (closure compositions). codes character vector store laboratory codes (unique identifiers). samples character vector store sample identifiers (allows duplicates case repeated measurements). groups character vector store group names (). parts character vector store part names. ratio character vector store ratio names. order integer vector store original ordering columns. base numeric matrix store basis transformation. weights numeric vector store weights assigned respective log-ratios.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Log-Ratio Matrix — LogRatio-class","text":"classes inherit matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Log-Ratio Matrix — LogRatio-class","text":"code snippets , x LogRatio object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Log-Ratio Matrix — LogRatio-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical Matrix — LogicalMatrix-class","title":"Logical Matrix — LogicalMatrix-class","text":"S4 classes represent \\(m \\times p\\) logical matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Logical Matrix — LogicalMatrix-class","text":".Data \\(m \\times p\\) logical matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Logical Matrix — LogicalMatrix-class","text":"class inherits matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Logical Matrix — LogicalMatrix-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Numeric Matrix — NumericMatrix-class","title":"Numeric Matrix — NumericMatrix-class","text":"S4 classes represent \\(m \\times p\\) numeric matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Numeric Matrix — NumericMatrix-class","text":".Data \\(m \\times p\\) numeric matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Numeric Matrix — NumericMatrix-class","text":"class inherits matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Numeric Matrix — NumericMatrix-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Outliers — OutlierIndex-class","title":"Outliers — OutlierIndex-class","text":"S4 class store result outlier detection.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Outliers — OutlierIndex-class","text":".Data logical matrix. codes character vector store laboratory codes (unique identifiers). samples character vector store sample identifiers (allows duplicates case repeated measurements). groups character vector store group names (). distances numeric matrix giving squared Mahalanobis distance. limit numeric value giving cut-value used outlier detection (quantile Chi-squared distribution). robust logical scalar: robust estimators used? dof (non-negative) numeric value giving degrees freedom.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Outliers — OutlierIndex-class","text":"classes inherit logical.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Outliers — OutlierIndex-class","text":"code snippets , x OutlierIndex object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Outliers — OutlierIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Summary Statistics of Data Subsets — aggregate","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"Splits data subsets, computes summary statistics , returns result.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"","code":"# S4 method for CompositionMatrix aggregate(x, by, FUN, ...)"},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"x CompositionMatrix object. character string specifying grouping element. must one \"samples\" \"groups\". unambiguous substring can given. FUN function compute summary statistics. ... arguments passed FUN.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"","code":"## Create a data.frame X <- data.frame(   samples = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"C\", \"C\", \"C\"),   groups = c(\"X\", \"X\", \"X\", \"X\", \"X\", \"X\", \"Y\", \"Y\", \"Y\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X)  ## Compositional mean by sample aggregate(Y, by = \"samples\", FUN = mean) #>          Ca        Fe         Na #> A 0.4468157 0.4539051 0.09927924 #> B 0.4746506 0.4742013 0.05114805 #> C 0.3067545 0.6238351 0.06941036  ## Metric variance by group aggregate(Y, by = \"groups\", FUN = metric_var) #>        [,1] #> X 0.2127884 #> Y 0.4275836"},{"path":"https://packages.tesselle.org/nexus/reference/arctic.html","id":null,"dir":"Reference","previous_headings":"","what":"Arctic Lake — arctic","title":"Arctic Lake — arctic","text":"Sand, silt, clay compositions 39 sediment samples different water depths Arctic lake.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arctic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arctic Lake — arctic","text":"","code":"arctic"},{"path":"https://packages.tesselle.org/nexus/reference/arctic.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Arctic Lake — arctic","text":"data.frame 4 variables: sand Sand content (percent). silt Silt content (percent). clay Clay content (percent). depth Water depth (m).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arctic.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Arctic Lake — arctic","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":null,"dir":"Reference","previous_headings":"","what":"Operations in the Simplex — arithmetic","title":"Operations in the Simplex — arithmetic","text":"Operators performing operations simplex.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Operations in the Simplex — arithmetic","text":"","code":"x %perturbe% y  x %power% y  # S4 method for CompositionMatrix,CompositionMatrix %perturbe%(x, y)  # S4 method for CompositionMatrix,numeric %power%(x, y)  # S4 method for numeric,CompositionMatrix %power%(x, y)"},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Operations in the Simplex — arithmetic","text":"x CompositionMatrix object. y CompositionMatrix object numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Operations in the Simplex — arithmetic","text":"CompositionMatrix object numeric vector (x).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Operations in the Simplex — arithmetic","text":"%perturbe% Perturbation operation. %power% Powering operation.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Operations in the Simplex — arithmetic","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Operations in the Simplex — arithmetic","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #>           V1        V2   V3 #> S1 0.4166667 0.8333333 0.75  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>     V1 V2  V3 #> S1 0.5  1 0.5  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to Amounts — as_amounts","title":"Coerce to Amounts — as_amounts","text":"Coerce Amounts","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to Amounts — as_amounts","text":"","code":"as_amounts(from, ...)  # S4 method for CompositionMatrix as_amounts(from)"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to Amounts — as_amounts","text":"CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to Amounts — as_amounts","text":"numeric  matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to Amounts — as_amounts","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to Amounts — as_amounts","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies get_totals(B) #>  [1] 202 285 198 262 325 277 141 243 338 197 247 192 261 345 271 201 124 182 210 #> [20] 411  ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to a data.frame X <- data.frame(B) head(X) #>           V1         V2        V3         V4         V5 #> S1 0.4306931 0.13366337 0.1930693 0.00990099 0.23267327 #> S2 0.3438596 0.06666667 0.1578947 0.09473684 0.33684211 #> S3 0.2525253 0.15151515 0.2373737 0.24242424 0.11616162 #> S4 0.1603053 0.15267176 0.2633588 0.16030534 0.26335878 #> S5 0.2707692 0.22461538 0.3015385 0.14153846 0.06153846 #> S6 0.2563177 0.33212996 0.1191336 0.05054152 0.24187726"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to a Closed Compositional Matrix — as_composition","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"Coerces object CompositionMatrix object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"","code":"as_composition(from, ...)  # S4 method for numeric as_composition(from)  # S4 method for matrix as_composition(from)  # S4 method for data.frame as_composition(   from,   codes = NULL,   samples = NULL,   groups = NULL,   auto = getOption(\"nexus.autodetect\"),   verbose = getOption(\"nexus.verbose\") )"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"matrix data.frame coerced. ... Currently used. codes integer giving index column used laboratory codes (unique identifiers). samples integer giving index column used sample identification: allows duplicates identify replicated measurements. NULL (default), row names used sample IDs. groups integer giving index column used group samples. NULL (default), grouping stored. auto logical scalar: try automatically detect codes, samples groups columns? verbose logical scalar: R report extra information progress?","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"CompositionMatrix object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"CompositionMatrix class special slots: codes laboratory codes, samples repeated measurements/observation, groups group data site/area. coercing data.frame CompositionMatrix object, attempt made automatically assign values slots mapping column names (case insensitive, plural insensitive). behavior can disabled setting options(nexus.autodetect = FALSE) overridden explicitly specifying columns used. See vignette(\"nexus\").","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"non-numeric variable removed.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies get_totals(B) #>  [1] 236 212 297 289 262 285 286 139 256 227 212 303 294 230 299 250 239 169 377 #> [20] 256  ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to a data.frame X <- data.frame(B) head(X) #>            V1        V2          V3          V4        V5 #> S1 0.11864407 0.1652542 0.296610169 0.008474576 0.4110169 #> S2 0.01415094 0.4056604 0.009433962 0.301886792 0.2688679 #> S3 0.19528620 0.3131313 0.279461279 0.080808081 0.1313131 #> S4 0.07612457 0.1764706 0.259515571 0.269896194 0.2179931 #> S5 0.09923664 0.2175573 0.221374046 0.106870229 0.3549618 #> S6 0.32982456 0.2210526 0.007017544 0.333333333 0.1087719"},{"path":"https://packages.tesselle.org/nexus/reference/as_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to Features — as_features","title":"Coerce to Features — as_features","text":"Converts object collection features.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to Features — as_features","text":"","code":"as_features(from, ...)  # S4 method for CompositionMatrix as_features(from)"},{"path":"https://packages.tesselle.org/nexus/reference/as_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to Features — as_features","text":"CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to Features — as_features","text":"data.frame informations extra columns.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/as_features.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to Features — as_features","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_features.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to Features — as_features","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies get_totals(B) #>  [1] 247 183 234 286 288 323 232 286 221 346 239 298 323 219 172 127 238 171 272 #> [20] 261  ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to a data.frame X <- data.frame(B) head(X) #>           V1         V2        V3         V4         V5 #> S1 0.2145749 0.37651822 0.1052632 0.14170040 0.16194332 #> S2 0.2896175 0.22950820 0.1092896 0.16393443 0.20765027 #> S3 0.4230769 0.14529915 0.2692308 0.01709402 0.14529915 #> S4 0.1258741 0.16783217 0.2517483 0.25524476 0.19930070 #> S5 0.2465278 0.15277778 0.2604167 0.32638889 0.01388889 #> S6 0.2569659 0.09597523 0.2043344 0.18885449 0.25386997"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Graph of Log-ratios — as_graph","title":"Graph of Log-ratios — as_graph","text":"Produces graph log-ratios.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graph of Log-ratios — as_graph","text":"","code":"as_graph(object, ...)  # S4 method for LR as_graph(object)  # S4 method for ALR as_graph(object)  # S4 method for ILR as_graph(object)"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Graph of Log-ratios — as_graph","text":"object LogRatio object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Graph of Log-ratios — as_graph","text":"igraph graph object.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Graph of Log-ratios — as_graph","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graph of Log-ratios — as_graph","text":"","code":"library(igraph) #>  #> Attaching package: ‘igraph’ #> The following objects are masked from ‘package:stats’: #>  #>     decompose, spectrum #> The following object is masked from ‘package:base’: #>  #>     union  ## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda) lr_graph <- as_graph(lr) plot(lr_graph)   ## Additive log-ratio alr <- transform_alr(coda) alr_graph <- as_graph(alr) plot(alr_graph)   ## Isometric log-ratio ilr <- transform_ilr(coda) ilr_graph <- as_graph(ilr) plot(ilr_graph)   plr <- transform_plr(coda) plr_graph <- as_graph(plr) plot(plr_graph)"},{"path":"https://packages.tesselle.org/nexus/reference/barplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Barplot of Compositional Data — barplot","title":"Barplot of Compositional Data — barplot","text":"Displays compositional bar chart.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/barplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Barplot of Compositional Data — barplot","text":"","code":"# S4 method for CompositionMatrix barplot(   height,   ...,   order = NULL,   decreasing = FALSE,   groups = get_groups(height),   horiz = TRUE,   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE )"},{"path":"https://packages.tesselle.org/nexus/reference/barplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Barplot of Compositional Data — barplot","text":"height CompositionMatrix object. ... parameters passed graphics::barplot(). order integer vector giving index column used ordering data. decreasing logical scalar: sort order increasing decreasing? groups factor sense .factor(groups) defines grouping. set, matrix panels defined groups drawn. horiz logical scalar. FALSE, bars drawn vertically first bar left. TRUE (default), bars drawn horizontally first bottom. xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot?","code":""},{"path":"https://packages.tesselle.org/nexus/reference/barplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Barplot of Compositional Data — barplot","text":"barplot() called side-effects: results graphic displayed (invisibly return height).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/barplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Barplot of Compositional Data — barplot","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/barplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Barplot of Compositional Data — barplot","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Bar plot barplot(coda, order = 2)   ## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  barplot(coda, order = 1, main = \"Kommos Ceramics\")  barplot(coda, order = 1, horiz = FALSE, main = \"Kommos Ceramics\")"},{"path":"https://packages.tesselle.org/nexus/reference/chemistry.html","id":null,"dir":"Reference","previous_headings":"","what":"Can Sora Chemical Data — chemistry","title":"Can Sora Chemical Data — chemistry","text":"Can Sora Chemical Data","code":""},{"path":"https://packages.tesselle.org/nexus/reference/chemistry.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Can Sora Chemical Data — chemistry","text":"","code":"chemistry"},{"path":"https://packages.tesselle.org/nexus/reference/chemistry.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Can Sora Chemical Data — chemistry","text":"data.frame 30 variables.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/chemistry.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Can Sora Chemical Data — chemistry","text":"Cau, M.-. (1999). Importaciones de cerámica tardorromana de cocina en las Iles Balears: el caso de Can Sora (Eivissa). J. Capel Martinez, Arqueometria y Arqueologia, p. 197-219. Granada: Editorial Universidad de Granada. Monografica Arte y Arqueología 47.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":null,"dir":"Reference","previous_headings":"","what":"Closure Operation — closure","title":"Closure Operation — closure","text":"Closes compositions sum 1.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Closure Operation — closure","text":"","code":"closure(x, ...)  # S4 method for numeric closure(x, total = 1, na.rm = FALSE)  # S4 method for matrix closure(x, total = 1, na.rm = FALSE)"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Closure Operation — closure","text":"x numeric vector matrix. ... Currently used. total numeric vector specifying total amount compositions closed (defaults 1). na.rm logical scalar: missing values removed?","code":""},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Closure Operation — closure","text":"numeric vector matrix (x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Closure Operation — closure","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Closure Operation — closure","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #>           V1        V2   V3 #> S1 0.4166667 0.8333333 0.75  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>     V1 V2  V3 #> S1 0.5  1 0.5  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":null,"dir":"Reference","previous_headings":"","what":"Covariance Matrix — covariance","title":"Covariance Matrix — covariance","text":"Computes (centered) log-ratio covariance matrix (see ).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Covariance Matrix — covariance","text":"","code":"covariance(x, ...)  # S4 method for CompositionMatrix covariance(x, center = TRUE, method = \"pearson\")  # S4 method for ALR covariance(x, method = \"pearson\")  # S4 method for CLR covariance(x, method = \"pearson\")"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Covariance Matrix — covariance","text":"x CompositionMatrix object. ... Currently used. center logical scalar: centered log-ratio covariance matrix computed? method character string indicating covariance computed (see stats::cov()).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Covariance Matrix — covariance","text":"matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"methods-by-class-","dir":"Reference","previous_headings":"","what":"Methods (by class)","title":"Covariance Matrix — covariance","text":"covariance(ALR): Computes log-ratio covariance matrix (Aitchison 1986, definition 4.5). covariance(CLR): Computes centered log-ratio covariance matrix (Aitchison 1986, definition 4.6).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Covariance Matrix — covariance","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Covariance Matrix — covariance","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Covariance Matrix — covariance","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Log-ratio covariance matrix ## (Aitchison 1986, definition 4.5) covariance(coda, center = FALSE) #>            A_E        B_E        C_E       D_E #> A_E  0.1385604  0.2641498 -0.2233304 0.1214018 #> B_E  0.2641498  0.6490135 -0.7020147 0.1444000 #> C_E -0.2233304 -0.7020147  0.9476375 0.0116220 #> D_E  0.1214018  0.1444000  0.0116220 0.1870579  ## Centered log-ratio covariance matrix ## (Aitchison 1986, definition 4.6) covariance(coda, center = TRUE) #>             A           B           C           D           E #> A  0.06443676  0.17907284 -0.24408053  0.01453821 -0.01396727 #> B  0.17907284  0.55298309 -0.73371823  0.02658296 -0.02492066 #> C -0.24408053 -0.73371823  0.98026080 -0.04186819  0.03940616 #> D  0.01453821  0.02658296 -0.04186819  0.04745430 -0.04670728 #> E -0.01396727 -0.02492066  0.03940616 -0.04670728  0.04618906"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Distances — dist","title":"Distances — dist","text":"Computes log-ratio variance matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distances — dist","text":"","code":"# S4 method for CompositionMatrix dist(x, method = \"euclidean\", diag = FALSE, upper = FALSE, p = 2)"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distances — dist","text":"x CompositionMatrix object. method character string specifying distance measure used. See stats::dist() available distances. diag logical scalar indicating whether diagonal distance matrix printed. upper logical scalar indicating whether upper triangle distance matrix printed. p integer giving power Minkowski distance.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distances — dist","text":"stats::dist object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distances — dist","text":"Distances computed CLR-transformed data.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Distances — dist","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distances — dist","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distances — dist","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Aitchison distance ## (euclidean distance between CLR-transformed compositions) d <- dist(coda)  ## Cluster dendrogram h <- hclust(d, method = \"ward.D2\") plot(h)"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Working With Groups — groups","title":"Working With Groups — groups","text":"Retrieves defines groups observations belong.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Working With Groups — groups","text":"","code":"any_assigned(x)  is_assigned(x)  get_groups(x)  set_groups(x) <- value  # S4 method for CompositionMatrix is_assigned(x)  # S4 method for LogRatio is_assigned(x)  # S4 method for OutlierIndex is_assigned(x)  # S4 method for CompositionMatrix any_assigned(x)  # S4 method for LogRatio any_assigned(x)  # S4 method for OutlierIndex any_assigned(x)  # S4 method for CompositionMatrix get_groups(x)  # S4 method for LogRatio get_groups(x)  # S4 method for OutlierIndex get_groups(x)  # S4 method for CompositionMatrix set_groups(x) <- value"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Working With Groups — groups","text":"x object get set groups. value possible value groups x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Working With Groups — groups","text":"set_groups() returns object sort x new group names assigned. get_groups() returns character vector giving group names x. any_assigned() returns logical scalar specifying whether x groups. is_assigned() returns logical vector specifying whether observation belongs group.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Working With Groups — groups","text":"See vignette(\"nexus\").","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Working With Groups — groups","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":null,"dir":"Reference","previous_headings":"","what":"Hongite Mineralogy — hongite","title":"Hongite Mineralogy — hongite","text":"Mineral compositions 25 rock specimens hongite type.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hongite Mineralogy — hongite","text":"","code":"hongite"},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Hongite Mineralogy — hongite","text":"data.frame 5 variables (minerals): Albite (percent). B Blandite (percent). C Cornite (percent). D Daubite (percent). E Endite (percent).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hongite Mineralogy — hongite","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/identifiers.html","id":null,"dir":"Reference","previous_headings":"","what":"Unique Identifiers — identifiers","title":"Unique Identifiers — identifiers","text":"Retrieves defines unique identifier (eg. laboratory codes) observation.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/identifiers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unique Identifiers — identifiers","text":"","code":"get_identifiers(x)  set_identifiers(x) <- value  # S4 method for CompositionMatrix get_identifiers(x)  # S4 method for LogRatio get_identifiers(x)  # S4 method for OutlierIndex get_identifiers(x)  # S4 method for CompositionMatrix set_identifiers(x) <- value"},{"path":"https://packages.tesselle.org/nexus/reference/identifiers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unique Identifiers — identifiers","text":"x object get set codes. value possible value codes x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/identifiers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unique Identifiers — identifiers","text":"set_identifiers() returns object sort x new identifiers assigned. get_identifiers() returns character vector giving unique identifiers x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/identifiers.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Unique Identifiers — identifiers","text":"See vignette(\"nexus\").","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/identifiers.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Unique Identifiers — identifiers","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":null,"dir":"Reference","previous_headings":"","what":"Mahalanobis Distance — mahalanobis","title":"Mahalanobis Distance — mahalanobis","text":"Computes squared Mahalanobis distance rows x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mahalanobis Distance — mahalanobis","text":"","code":"# S4 method for CompositionMatrix mahalanobis(x, center, cov, ..., robust = TRUE, method = c(\"mve\", \"mcd\"))  # S4 method for ILR mahalanobis(x, center, cov, ..., robust = TRUE, method = c(\"mve\", \"mcd\"))"},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mahalanobis Distance — mahalanobis","text":"x CompositionMatrix ILR object. center numeric vector giving mean vector distribution. missing, estimated x. cov numeric matrix giving covariance distribution. missing, estimated x. ... Extra parameters passed MASS::cov.rob(). used robust TRUE. robust logical scalar: robust location scatter estimation used? method character string specifying method used. must one \"mve\" (minimum volume ellipsoid) \"mcd\" (minimum covariance determinant). used robust TRUE.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mahalanobis Distance — mahalanobis","text":"numeric vector.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mahalanobis Distance — mahalanobis","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mahalanobis Distance — mahalanobis","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Mahalanobis distance mahalanobis(coda) #>  #>         H1         H2         H3         H4         H5         H6         H7  #>  4.4350697  0.5293921  2.2463414  1.3647605 13.0184551  5.8324688 18.0231947  #>         H8         H9        H10        H11        H12        H13        H14  #> 25.4910136  1.4264228 18.0221709  2.0490574  5.4416081  5.0114990  6.3602144  #>        H15        H16        H17        H18        H19        H20        H21  #>  1.9635308  6.4062999  7.3866770  1.5149231  2.9208378  4.7044107  1.1393424  #>        H22        H23        H24        H25  #>  1.6158258 10.0169365  2.7827184  4.8516637"},{"path":"https://packages.tesselle.org/nexus/reference/margin.html","id":null,"dir":"Reference","previous_headings":"","what":"Marginal Compositions — margin","title":"Marginal Compositions — margin","text":"Marginal Compositions","code":""},{"path":"https://packages.tesselle.org/nexus/reference/margin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Marginal Compositions — margin","text":"","code":"margin(x, ...)  # S4 method for CompositionMatrix margin(x, parts = c(1, 2), name = \"*\")"},{"path":"https://packages.tesselle.org/nexus/reference/margin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Marginal Compositions — margin","text":"x CompositionMatrix object. ... Currently used. parts integer character vector specifying columns selected. name character string giving name amalgamation column.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/margin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Marginal Compositions — margin","text":"CompositionMatrix object.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/margin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Marginal Compositions — margin","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/margin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Marginal Compositions — margin","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Marginal compositions mar <- margin(coda, parts = c(\"B\", \"D\")) head(mar) #> <CompositionMatrix: 6 x 3> #>            B         D         * #> H1 0.6328342 0.1277646 0.2394011 #> H2 0.4837434 0.1869933 0.3292633 #> H3 0.2163720 0.2258829 0.5577451 #> H4 0.4936486 0.2094895 0.2968618 #> H5 0.6888621 0.1384919 0.1726460 #> H6 0.5360281 0.2557386 0.2082334"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Compositional Mean — mean","title":"Compositional Mean — mean","text":"Compositional Mean","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compositional Mean — mean","text":"","code":"# S4 method for CompositionMatrix mean(x, ..., na.rm = FALSE)"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compositional Mean — mean","text":"x CompositionMatrix object. ... Currently used. na.rm logical scalar: missing values removed?","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compositional Mean — mean","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compositional Mean — mean","text":"Closed vector columns geometric means.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compositional Mean — mean","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compositional Mean — mean","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compositional Mean — mean","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Mean mean(coda) #>          A          B          C          D          E  #> 0.48941415 0.21966082 0.09881294 0.10405382 0.08805827   ## Metric variance metric_var(coda) #> [1] 1.691324  ## Metric standard deviation metric_sd(coda) #> [1] 0.6502546"},{"path":"https://packages.tesselle.org/nexus/reference/metric_var.html","id":null,"dir":"Reference","previous_headings":"","what":"Metric Variance and Standard Deviation — metric_var","title":"Metric Variance and Standard Deviation — metric_var","text":"metric_var() computes metric variance (total variance), .e. global measure spread. metric_sd() computes metric standard deviation.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/metric_var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metric Variance and Standard Deviation — metric_var","text":"","code":"metric_var(x, ...)  metric_sd(x, ...)  # S4 method for CompositionMatrix metric_var(x)  # S4 method for CompositionMatrix metric_sd(x)"},{"path":"https://packages.tesselle.org/nexus/reference/metric_var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metric Variance and Standard Deviation — metric_var","text":"x CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/metric_var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metric Variance and Standard Deviation — metric_var","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/metric_var.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Metric Variance and Standard Deviation — metric_var","text":"metric variance average CLR variances.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/metric_var.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Metric Variance and Standard Deviation — metric_var","text":"Boogaart, K. G. van den & Tolosana-Delgado, R. (2013). Analyzing Compositional Data R. Berlin Heidelberg: Springer-Verlag. doi:10.1007/978-3-642-36809-7 . Hron, K. & Kubáček. L. (2011). Statistical Properties Total Variation Estimator Compositional Data. Metrika, 74 (2): 221-230. doi:10.1007/s00184-010-0299-3 . Pawlowsky-Glahn, V. & Egozcue, J. J. (2001). Geometric Approach Statistical Analysis Simplex. Stochastic Environmental Research Risk Assessment, 15(5): 384-398. doi:10.1007/s004770100077 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/metric_var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Metric Variance and Standard Deviation — metric_var","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/metric_var.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metric Variance and Standard Deviation — metric_var","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Mean mean(coda) #>          A          B          C          D          E  #> 0.48941415 0.21966082 0.09881294 0.10405382 0.08805827   ## Metric variance metric_var(coda) #> [1] 1.691324  ## Metric standard deviation metric_sd(coda) #> [1] 0.6502546"},{"path":"https://packages.tesselle.org/nexus/reference/mix.html","id":null,"dir":"Reference","previous_headings":"","what":"Mixed-Mode Analysis — mix","title":"Mixed-Mode Analysis — mix","text":"Mixes chemical petrographic matrices.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mixed-Mode Analysis — mix","text":"","code":"mix(x, y, ...)  # S4 method for matrix,matrix mix(x, y, lambda = 1, ...)  # S4 method for dist,dist mix(x, y, mu = 0.5)"},{"path":"https://packages.tesselle.org/nexus/reference/mix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mixed-Mode Analysis — mix","text":"x matrix chemical compositional data dissimilarity matrix chemical compositional data. y matrix coded mineralogical binary data dissimilarity matrix mineralogical data. ... Extra parameters passed cluster::daisy(). lambda length-one numeric vector giving weighting factor. mu length-one numeric vector lies 0 1 giving mixing parameter.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mixed-Mode Analysis — mix","text":"stats::dist object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mix.html","id":"methods-by-class-","dir":"Reference","previous_headings":"","what":"Methods (by class)","title":"Mixed-Mode Analysis — mix","text":"mix(x = matrix, y = matrix): First approach mixed-mode analysis. mix(x = dist, y = dist): Second approach mixed-mode analysis.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mix.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mixed-Mode Analysis — mix","text":"Experimental.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mix.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Mixed-Mode Analysis — mix","text":"Baxter, M. J., Beardah, C. C., Papageorgiou, ., Cau, M. ., Day, P. M. & Kilikoglou, V. (2008). Statistical Approaches Study Ceramic Artefacts Using Geochemical Petrographic Data. Archaeometry, 50(1): 142-157. doi:10.1111/j.1475-4754.2007.00359.x . Beardah, C. C., Baxter, M. J., Papageorgiou, . & Cau, M. . (2003). \"Mixed-Mode\" Approaches Grouping Ceramic Artefacts Using S-Plus. M. Doerr . Sarris, Digital Heritage Archaeology, p. 261-266. Athens: Archive Monuments Publications, Hellenic Ministry Culture. Gower, J. C. (1971). general coefficient similarity properties. Biometrics, 27(4):857-874. doi:10.2307/2528823 .","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mix.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mixed-Mode Analysis — mix","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mixed-Mode Analysis — mix","text":"","code":"# \\donttest{ ## Prepare chemical data data(\"chemistry\") major <- c(\"Fe2O3\", \"Al2O3\", \"MnO\", \"P2O5\", \"TiO2\", \"MgO\", \"CaO\", \"Na2O\", \"K2O\", \"SiO2\") chem <- chemistry[-1, major]  ## Prepare petrographic data data(\"petrography\") petro <- petrography[-c(7, 8), -1] petro <- cdt(petro) # Get the complete disjunctive table  ## First approach mix1 <- mix(as.matrix(chem), as.matrix(petro), lambda = 2) #> Warning: binary variable(s) 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 treated as interval scaled mds1 <- stats::cmdscale(mix1) # Multi-Dimensional Scaling plot(mds1)  # }"},{"path":"https://packages.tesselle.org/nexus/reference/nexus-package.html","id":null,"dir":"Reference","previous_headings":"","what":"nexus: Sourcing Archaeological Materials by Chemical Composition — nexus-package","title":"nexus: Sourcing Archaeological Materials by Chemical Composition — nexus-package","text":"Exploration analysis compositional data framework Aitchison (1986, ISBN: 978-94-010-8324-9). package provides tools chemical fingerprinting source tracking ancient materials.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/nexus-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package options","title":"nexus: Sourcing Archaeological Materials by Chemical Composition — nexus-package","text":"nexus uses following options() configure behavior: nexus.autodetect: logical scalar. Try automatically assign values corresponding slot CompositionMatrix object coercing data.frame? Defaults TRUE. nexus.verbose: logical scalar. R report extra information progress? Defaults TRUE.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/nexus-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"nexus: Sourcing Archaeological Materials by Chemical Composition — nexus-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Archéosciences Bordeaux (UMR 6034) Maison de l'Archéologie Université Bordeaux Montaigne F-33607 Pessac cedex France","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Outlier Detection — outliers","title":"Outlier Detection — outliers","text":"Outlier Detection","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Outlier Detection — outliers","text":"","code":"outliers(object, ...)  # S4 method for CompositionMatrix outliers(   object,   ...,   groups = get_groups(object),   robust = TRUE,   method = c(\"mve\", \"mcd\"),   quantile = 0.975 )"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Outlier Detection — outliers","text":"object CompositionMatrix. ... Extra parameters passed MASS::cov.rob(). used robust TRUE. groups factor sense .factor(groups) defines grouping. set, XXX. robust logical scalar: robust location scatter estimation used? method character string specifying method used. must one \"mve\" (minimum volume ellipsoid) \"mcd\" (minimum covariance determinant). used robust TRUE. quantile length-one numeric vector giving significance level. quantile used cut-value outlier detection: observations larger (squared) Mahalanobis distance considered potential outliers.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Outlier Detection — outliers","text":"OutlierIndex object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Outlier Detection — outliers","text":"outlier can defined large Mahalanobis distance observations. way, certain proportion observations can identified, e.g. top 2% values (.e. values 0.98th percentile Chi-2 distribution). one hand, Mahalanobis distance likely strongly affected presence outliers. Rousseeuw van Zomeren (1990) thus recommend using robust methods (excessively affected presence outliers). hand, choice threshold classifying observation outlier discussed. apparent reason particular threshold applicable data sets (Filzmoser, Garrett, Reimann 2005).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Outlier Detection — outliers","text":"Filzmoser, P., Garrett, R. G. & Reimann, C. (2005). Multivariate outlier detection exploration geochemistry. Computers & Geosciences, 31(5), 579-587. doi:10.1016/j.cageo.2004.11.013 . Filzmoser, P. & Hron, K. (2008). Outlier Detection Compositional Data Using Robust Methods. Mathematical Geosciences, 40(3), 233-248. doi:10.1007/s11004-007-9141-5 . Filzmoser, P., Hron, K. & Reimann, C. (2012). Interpretation multivariate outliers compositional data. Computers & Geosciences, 39, 77-85. doi:10.1016/j.cageo.2011.06.014 . Rousseeuw, P. J. & van Zomeren, B. C. (1990). Unmasking Multivariate Outliers Leverage Points. Journal American Statistical Association, 85(411): 633-639. doi:10.1080/01621459.1990.10474920 . Santos, F. (2020). Modern methods old data: overview robust methods outliers detection applications osteology. Journal Archaeological Science: Reports, 32, 102423. doi:10.1016/j.jasrep.2020.102423 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Outlier Detection — outliers","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Outlier Detection — outliers","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  ## Detect outliers out <- outliers(coda, groups = NULL, robust = FALSE)  plot(out) # Plot  plot(out, qq = TRUE) # Quantile-Quantile plot   ## Detect outliers by group out <- outliers(coda[, 1:15, drop = FALSE])  plot(out, ncol = 2) # Plot  plot(out, qq = TRUE, ncol = 4) # Quantile-Quantile plot"},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Components Analysis — pca_coda","title":"Principal Components Analysis — pca_coda","text":"Computes principal components analysis based singular value decomposition.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Components Analysis — pca_coda","text":"","code":"# S4 method for CompositionMatrix pca(   object,   center = TRUE,   scale = FALSE,   rank = NULL,   sup_row = NULL,   sup_col = NULL,   weight_row = NULL,   weight_col = NULL )  # S4 method for LogRatio pca(   object,   center = TRUE,   scale = FALSE,   rank = NULL,   sup_row = NULL,   sup_col = NULL,   weight_row = NULL,   weight_col = NULL )"},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Components Analysis — pca_coda","text":"object LogRatio object. center logical scalar: variables shifted zero centered? scale logical scalar: variables scaled unit variance? rank integer value specifying maximal number components kept results. NULL (default), \\(p - 1\\) components returned. sup_row vector specifying indices supplementary rows. sup_col vector specifying indices supplementary columns. weight_row numeric vector specifying active row (individual) weights. NULL (default), uniform weights used. Row weights internally normalized sum 1 weight_col numeric vector specifying active column (variable) weights. NULL (default), uniform weights (1) used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Components Analysis — pca_coda","text":"dimensio::PCA object. See package dimensio details.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Principal Components Analysis — pca_coda","text":"Aitchison, J. Greenacre, M. (2002). Biplots compositional data. Journal Royal Statistical Society: Series C (Applied Statistics), 51: 375-392. doi:10.1111/1467-9876.00275 . Filzmoser, P., Hron, K. Reimann, C. (2009). Principal component analysis compositional data outliers. Environmetrics, 20: 621-632. doi:10.1002/env.966 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Principal Components Analysis — pca_coda","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Components Analysis — pca_coda","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  ## Centered log-ratio clr <- transform_clr(coda)  ## PCA X <- pca(clr, scale = FALSE)  ## Explore results library(dimensio)  ## Plot viz_individuals(X, highlight = get_groups(coda), pch = 16)  viz_variables(X)"},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":null,"dir":"Reference","previous_headings":"","what":"Perturbation Operation — perturbation","title":"Perturbation Operation — perturbation","text":"Perturbation two compositions.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perturbation Operation — perturbation","text":"","code":"perturbation(x, y, ...)  # S4 method for numeric,numeric perturbation(x, y)  # S4 method for CompositionMatrix,numeric perturbation(x, y)  # S4 method for CompositionMatrix,matrix perturbation(x, y)"},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perturbation Operation — perturbation","text":"x, y numeric vector compositional data CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perturbation Operation — perturbation","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perturbation Operation — perturbation","text":"compositional geometry, perturbation plays role sum (translation). closed component-wise product two compositions.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Perturbation Operation — perturbation","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perturbation Operation — perturbation","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #>           V1        V2   V3 #> S1 0.4166667 0.8333333 0.75  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>     V1 V2  V3 #> S1 0.5  1 0.5  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/petrography.html","id":null,"dir":"Reference","previous_headings":"","what":"Can Sora Petrographic Data — petrography","title":"Can Sora Petrographic Data — petrography","text":"Can Sora Petrographic Data","code":""},{"path":"https://packages.tesselle.org/nexus/reference/petrography.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Can Sora Petrographic Data — petrography","text":"","code":"petrography"},{"path":"https://packages.tesselle.org/nexus/reference/petrography.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Can Sora Petrographic Data — petrography","text":"data.frame 21 variables: VAR1 Optical activity. VAR2 Inclusion orientation. VAR3 Void orientation. VAR4 Texture. VAR5 Special components. VAR6 Plutonic rocks. VAR7 Volcanic rocks. VAR8 Metamorphic rocks. VAR9 Sedimentary rocks. VAR10 Quartz. VAR11 Feldspar. VAR12 Plagioclase. VAR13 Pyroxenes. VAR14 Amphiboles. VAR15 Micas. VAR16 Phyllosilicates. VAR17 Carbonates. VAR18 constituents. VAR19 Packing.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/petrography.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Can Sora Petrographic Data — petrography","text":"Cau, M.-., Day, P. M., Baxter, M. J., Papageorgiou, ., Iliopoulos, . & Montana, G. (2004). Exploring Automatic Grouping Procedures Ceramic Petrology. Journal Archaeological Science, 31(9): 1325-1338. doi:10.1016/j.jas.2004.03.006 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Compositional Data — plot","title":"Plot Compositional Data — plot","text":"Displays matrix ternary plots.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Compositional Data — plot","text":"","code":"# S4 method for CompositionMatrix,missing plot(x, ..., margin = NULL)"},{"path":"https://packages.tesselle.org/nexus/reference/plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Compositional Data — plot","text":"x CompositionMatrix object. ... graphical parameters. margin character string integer giving index column used third part ternary plots. NULL (default), marginal compositions used (.e. geometric mean non-selected parts).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Compositional Data — plot","text":"plot() called side-effects: results graphic displayed (invisibly return x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Compositional Data — plot","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Compositional Data — plot","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Ternary plots plot(coda)"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Log-Ratios — plot_logratio","title":"Plot Log-Ratios — plot_logratio","text":"Displays density plot.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Log-Ratios — plot_logratio","text":"","code":"# S4 method for LogRatio,missing plot(   x,   ...,   order = NULL,   decreasing = FALSE,   groups = get_groups(x),   rug = TRUE,   ticksize = 0.05,   ncol = NULL,   flip = FALSE,   xlab = NULL,   ylab = NULL,   main = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   legend = list(x = \"topright\") )"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Log-Ratios — plot_logratio","text":"x LogRatio object. ... graphical parameters, particularly, border col. order logical scalar: ratio ordered? decreasing logical scalar: sort order increasing decreasing? groups factor sense .factor(groups) defines grouping. set, matrix panels defined groups drawn. rug logical scalar: rug representation (1-d plot) data added plot? ticksize length-one numeric vector giving length ticks making rug. Positive lengths give inwards ticks. used rug TRUE. ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) variable variable? xlab, ylab character vector giving x y axis labels. main character string giving main title plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? legend list additional arguments passed graphics::legend(); names list used argument names. NULL, legend displayed.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Log-Ratios — plot_logratio","text":"plot() called side-effects: results graphic displayed (invisibly return x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Log-Ratios — plot_logratio","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Log-Ratios — plot_logratio","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  ## Log ratio clr <- transform_clr(coda) plot(clr, group = NULL, flip = TRUE, border = \"black\", col = NA)  plot(clr, flip = TRUE)"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Outliers — plot_outliers","title":"Plot Outliers — plot_outliers","text":"Plot Outliers","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Outliers — plot_outliers","text":"","code":"# S4 method for OutlierIndex,missing plot(   x,   ...,   qq = FALSE,   probs = c(0.25, 0.75),   ncol = NULL,   flip = FALSE,   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL )"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Outliers — plot_outliers","text":"x OutlierIndex object. ... graphical parameters. qq logical scalar: quantile-quantile plot produced? probs length-two numeric vector representing probabilities. Corresponding quantile pairs define line drawn (see stats::qqline()). used qq TRUE. ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) group group? xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Outliers — plot_outliers","text":"plot() called side-effects: results graphic displayed (invisibly return x).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot Outliers — plot_outliers","text":"Filzmoser, P., Garrett, R. G. & Reimann, C. (2005). Multivariate outlier detection exploration geochemistry. Computers & Geosciences, 31(5), 579-587. doi:10.1016/j.cageo.2004.11.013 . Filzmoser, P. & Hron, K. (2008). Outlier Detection Compositional Data Using Robust Methods. Mathematical Geosciences, 40(3), 233-248. doi:10.1007/s11004-007-9141-5 . Filzmoser, P., Hron, K. & Reimann, C. (2012). Interpretation multivariate outliers compositional data. Computers & Geosciences, 39, 77-85. doi:10.1016/j.cageo.2011.06.014 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Outliers — plot_outliers","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Outliers — plot_outliers","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  ## Detect outliers out <- outliers(coda, groups = NULL, robust = FALSE)  plot(out) # Plot  plot(out, qq = TRUE) # Quantile-Quantile plot   ## Detect outliers by group out <- outliers(coda[, 1:15, drop = FALSE])  plot(out, ncol = 2) # Plot  plot(out, qq = TRUE, ncol = 4) # Quantile-Quantile plot"},{"path":"https://packages.tesselle.org/nexus/reference/policy.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing Values Policy — policy","title":"Missing Values Policy — policy","text":"Missing Values Policy","code":""},{"path":"https://packages.tesselle.org/nexus/reference/policy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Missing Values Policy — policy","text":"Compositional data quantitative (positive) descriptions parts whole, carrying relative, rather absolute, information (ie. relative changes relevant; Aitchison 1986). Basically, two situations can outlined: presence zeros: considered observed quantities, happen detection limit (thus interpreted small unknown values). presence missing values (NA): indicate quantities question observed. creating CompositionMatrix object, presence zero NA values allowed: makes possible explore visualize data preserving missing structure. However, user must deal missing values proceeding (e.g. removing incomplete cases replacing values concerned): log-ratio transformations computed presence missing values.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/policy.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Missing Values Policy — policy","text":"need advanced features (e.g. imputation missing values), consider compositions robCompositions package.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/policy.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Missing Values Policy — policy","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":null,"dir":"Reference","previous_headings":"","what":"Powering Operation — powering","title":"Powering Operation — powering","text":"Perturbation two compositions.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Powering Operation — powering","text":"","code":"powering(x, a, ...)  # S4 method for numeric,numeric powering(x, a)  # S4 method for CompositionMatrix,numeric powering(x, a)"},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Powering Operation — powering","text":"x numeric vector compositional data CompositionMatrix object. numeric constant. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Powering Operation — powering","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Powering Operation — powering","text":"compositional geometry, powering replaces product vector scalar (scaling) defined closed powering components given scalar.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Powering Operation — powering","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Powering Operation — powering","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #>           V1        V2   V3 #> S1 0.4166667 0.8333333 0.75  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>     V1 V2  V3 #> S1 0.5  1 0.5  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. arkhe remove_NA, remove_zero, replace_NA, replace_zero dimensio cdt, pca","code":""},{"path":"https://packages.tesselle.org/nexus/reference/replace_NA.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing Values Replacement — missing","title":"Missing Values Replacement — missing","text":"Multiplicative replacement missing values.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/replace_NA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing Values Replacement — missing","text":"","code":"# S4 method for CompositionMatrix replace_NA(x, value)"},{"path":"https://packages.tesselle.org/nexus/reference/replace_NA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing Values Replacement — missing","text":"x CompositionMatrix object. value numeric vector giving replacement values.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/replace_NA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing Values Replacement — missing","text":"CompositionMatrix object, missing values replaced.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/replace_NA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Missing Values Replacement — missing","text":"Martín-Fernández, J. ., Barceló-Vidal, C. & Pawlowsky-Glahn, V. (2003). Dealing Zeros Missing Values Compositional Data Sets Using Nonparametric Imputation. Mathematical Geology, 35(3): 253-278. doi:10.1023/:1023866030544 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/replace_NA.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Missing Values Replacement — missing","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/replace_NA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing Values Replacement — missing","text":"","code":"## Data from Martín-Fernández et al. 2003 X <- data.frame(   X1 = c(0.0000, 0.1304, 0.1963),   X2 = c(0.1250, 0.3151, NA),   X3 = c(0.1237, NA, NA),   X4 = c(0.7253, 0.2002, 0.0819),   X5 = c(0.0260, 0.3543, 0.0114) )  ## Coerce to a compositional matrix Y <- as_composition(X)  ## Replace zeros Z <- replace_NA(Y, value = 0.2) Z #> <CompositionMatrix: 3 x 5> #>          X1      X2     X3        X4         X5 #> 1 0.0000000 0.12500 0.1237 0.7253000 0.02600000 #> 2 0.1043200 0.25208 0.2000 0.1601600 0.28344000 #> 3 0.4066989 0.20000 0.2000 0.1696823 0.02361878"},{"path":"https://packages.tesselle.org/nexus/reference/replace_zero.html","id":null,"dir":"Reference","previous_headings":"","what":"Zero-Replacement — zero","title":"Zero-Replacement — zero","text":"Multiplicative replacement zeros.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/replace_zero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zero-Replacement — zero","text":"","code":"# S4 method for CompositionMatrix replace_zero(x, value, delta = 2/3)"},{"path":"https://packages.tesselle.org/nexus/reference/replace_zero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zero-Replacement — zero","text":"x CompositionMatrix object. value numeric vector giving detection limits part (\\((0,1)\\)). delta numeric vector specifying fraction detection limit used replacement.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/replace_zero.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Zero-Replacement — zero","text":"CompositionMatrix object, zero values replaced.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/replace_zero.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Zero-Replacement — zero","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. Martín-Fernández, J. ., Barceló-Vidal, C. & Pawlowsky-Glahn, V. (2003). Dealing Zeros Missing Values Compositional Data Sets Using Nonparametric Imputation. Mathematical Geology, 35(3): 253-278. doi:10.1023/:1023866030544 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/replace_zero.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Zero-Replacement — zero","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/replace_zero.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Zero-Replacement — zero","text":"","code":"## Data from Martín-Fernández et al. 2003 X <- data.frame(   X1 = c(0.0000, 0.1304, 0.1963),   X2 = c(0.1250, 0.3151, NA),   X3 = c(0.1237, NA, NA),   X4 = c(0.7253, 0.2002, 0.0819),   X5 = c(0.0260, 0.3543, 0.0114) )  ## Coerce to a compositional matrix Y <- as_composition(X)  ## Replace zeros Z <- replace_zero(Y, value = 0.02, delta = 2/3) Z #> <CompositionMatrix: 3 x 5> #>           X1        X2        X3        X4         X5 #> 1 0.01333333 0.1233333 0.1220507 0.7156293 0.02565333 #> 2 0.13040000 0.3151000        NA 0.2002000 0.35430000 #> 3 0.67783149        NA        NA 0.2828039 0.03936464"},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Working With Samples — samples","title":"Working With Samples — samples","text":"Retrieves defines sample names.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Working With Samples — samples","text":"","code":"any_replicated(x)  is_replicated(x)  get_samples(x)  set_samples(x) <- value  # S4 method for CompositionMatrix is_replicated(x)  # S4 method for LogRatio is_replicated(x)  # S4 method for OutlierIndex is_replicated(x)  # S4 method for CompositionMatrix any_replicated(x)  # S4 method for LogRatio any_replicated(x)  # S4 method for OutlierIndex any_replicated(x)  # S4 method for CompositionMatrix get_samples(x)  # S4 method for LogRatio get_samples(x)  # S4 method for OutlierIndex get_samples(x)  # S4 method for CompositionMatrix set_samples(x) <- value"},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Working With Samples — samples","text":"x object get set samples. value possible value samples x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Working With Samples — samples","text":"set_samples() returns object sort x new sample names assigned. get_samples() returns character vector giving sample names x. any_replicated() returns logical scalar specifying whether x replicated observations. is_replicated() returns logical vector specifying whether observation replicate.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Working With Samples — samples","text":"situations, measurements may repeated (e.g. multiple chemical analyses sample). presence repeated measurements can specified giving several observations sample name. See vignette(\"nexus\").","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Working With Samples — samples","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":null,"dir":"Reference","previous_headings":"","what":"Scalar Product — scalar","title":"Scalar Product — scalar","text":"Computes Aitchison scalar product two compositions.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scalar Product — scalar","text":"","code":"scalar(x, y, ...)  # S4 method for numeric,numeric scalar(x, y)  # S4 method for CompositionMatrix,CompositionMatrix scalar(x, y)"},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scalar Product — scalar","text":"x, y CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scalar Product — scalar","text":"numeric vector.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Scalar Product — scalar","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scalar Product — scalar","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #>           V1        V2   V3 #> S1 0.4166667 0.8333333 0.75  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>     V1 V2  V3 #> S1 0.5  1 0.5  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/scale.html","id":null,"dir":"Reference","previous_headings":"","what":"Scaling and Centering of Compositional Data — scale","title":"Scaling and Centering of Compositional Data — scale","text":"Scaling Centering Compositional Data","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scaling and Centering of Compositional Data — scale","text":"","code":"# S4 method for CompositionMatrix scale(x, center = TRUE, scale = TRUE)"},{"path":"https://packages.tesselle.org/nexus/reference/scale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scaling and Centering of Compositional Data — scale","text":"x CompositionMatrix object. center logical scalar numeric vector giving center substracted. scale logical scalar length-one numeric vector giving scaling factor multiplication.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scaling and Centering of Compositional Data — scale","text":"CompositionMatrix object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scale.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Scaling and Centering of Compositional Data — scale","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. Boogaart, K. G. van den & Tolosana-Delgado, R. (2013). Analyzing Compositional Data R. Berlin Heidelberg: Springer-Verlag. doi:10.1007/978-3-642-36809-7 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/scale.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Scaling and Centering of Compositional Data — scale","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scaling and Centering of Compositional Data — scale","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Center and scale scaled <- scale(coda, center = TRUE, scale = TRUE) mean(scaled) #>   A   B   C   D   E  #> 0.2 0.2 0.2 0.2 0.2  head(scaled) #> <CompositionMatrix: 6 x 5> #>             A          B          C          D          E #> H1 0.21634296 0.40851878 0.04204451 0.09427149 0.23882226 #> H2 0.19541817 0.23027129 0.17084651 0.16234092 0.24112312 #> H3 0.05406075 0.01921842 0.73935593 0.07477711 0.11258779 #> H4 0.23277503 0.24975389 0.12625644 0.20672500 0.18448965 #> H5 0.17430188 0.54016862 0.02523291 0.12375232 0.13654426 #> H6 0.25315982 0.30580853 0.05187680 0.30958887 0.07956597"},{"path":"https://packages.tesselle.org/nexus/reference/slides.html","id":null,"dir":"Reference","previous_headings":"","what":"Thin Sections — slides","title":"Thin Sections — slides","text":"Mineral compositions five slides reported five analysts.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/slides.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Thin Sections — slides","text":"","code":"slides"},{"path":"https://packages.tesselle.org/nexus/reference/slides.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Thin Sections — slides","text":"data.frame 9 variables: analyst Analyst number. slide Slide number. quartz Quartz (percent). microcline Microcline (percent). plagioclass Plagioclass (percent). biotite Biotite (percent). plagioclass Plagioclass (percent). muscovite Muscovite (percent). opaques Opaque minerals (percent). nonopaques Non-opaque minerals (percent).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/slides.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Thin Sections — slides","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or Replace Parts of an Object — subset","title":"Extract or Replace Parts of an Object — subset","text":"Operators acting objects extract replace parts.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or Replace Parts of an Object — subset","text":"","code":"# S4 method for CompositionMatrix,missing,missing,missing [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,missing,missing,logical [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,index,missing,missing [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,index,missing,logical [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,missing,index,missing [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,missing,index,logical [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,index,index,missing [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,index,index,logical [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix [(x, i, j, ...) <- value  # S4 method for CompositionMatrix [[(x, i, j, ...) <- value"},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or Replace Parts of an Object — subset","text":"x object extract element(s) replace element(s). , j Indices specifying elements extract replace. Indices numeric, integer character vectors empty (missing) NULL. Numeric values coerced integer .integer(). Character vectors matched name elements. empty index (comma separated blank) indicates entries dimension selected. ... Currently used. drop logical scalar: result coerced lowest possible dimension? works extracting elements, replacement. value possible value element(s) x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or Replace Parts of an Object — subset","text":"subsetted object sort x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"subcomposition","dir":"Reference","previous_headings":"","what":"Subcomposition","title":"Extract or Replace Parts of an Object — subset","text":"drop FALSE, subsetting possible components CompositionMatrix object produce closed subcomposition (see examples).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or Replace Parts of an Object — subset","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract or Replace Parts of an Object — subset","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite) head(coda) #> <CompositionMatrix: 6 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093 #> H2 0.482 0.238 0.090 0.092 0.098 #> H3 0.370 0.091 0.342 0.095 0.102 #> H4 0.509 0.238 0.072 0.101 0.080 #> H5 0.442 0.383 0.029 0.077 0.069 #> H6 0.523 0.262 0.042 0.125 0.048  ## Subset coda[[1, 1]] # Get the first value #> [1] 0.488 coda[1] # Get the first value #> [1] 0.488 coda[, ] # Get all values #> <CompositionMatrix: 25 x 5> #>             A         B          C          D          E #> H1  0.4880000 0.3170000 0.03800000 0.06400000 0.09300000 #> H2  0.4820000 0.2380000 0.09000000 0.09200000 0.09800000 #> H3  0.3700000 0.0910000 0.34200000 0.09500000 0.10200000 #> H4  0.5090000 0.2380000 0.07200000 0.10100000 0.08000000 #> H5  0.4420000 0.3830000 0.02900000 0.07700000 0.06900000 #> H6  0.5230000 0.2620000 0.04200000 0.12500000 0.04800000 #> H7  0.4460000 0.3300000 0.04600000 0.12200000 0.05600000 #> H8  0.3460000 0.0520000 0.42900000 0.09600000 0.07700000 #> H9  0.4120000 0.1170000 0.26700000 0.09600000 0.10800000 #> H10 0.4260000 0.4660000 0.00700000 0.05600000 0.04500000 #> H11 0.4990000 0.1950000 0.11400000 0.09500000 0.09700000 #> H12 0.4520000 0.3730000 0.02700000 0.05500000 0.09300000 #> H13 0.3270000 0.0850000 0.38900000 0.08000000 0.11900000 #> H14 0.4140000 0.1290000 0.23400000 0.15800000 0.06500000 #> H15 0.4620000 0.1750000 0.15800000 0.08300000 0.12200000 #> H16 0.3230000 0.0730000 0.40900000 0.12900000 0.06600000 #> H17 0.4320000 0.4430000 0.01000000 0.07800000 0.03700000 #> H18 0.4954955 0.3233233 0.03103103 0.08708709 0.06306306 #> H19 0.4230000 0.1580000 0.20400000 0.08300000 0.13200000 #> H20 0.4460000 0.1150000 0.23800000 0.11600000 0.08500000 #> H21 0.4580000 0.1660000 0.16800000 0.12000000 0.08800000 #> H22 0.4990000 0.2500000 0.06800000 0.10900000 0.07400000 #> H23 0.4860000 0.3400000 0.02500000 0.09400000 0.05500000 #> H24 0.4550000 0.1660000 0.17600000 0.09600000 0.10700000 #> H25 0.4590000 0.2490000 0.09700000 0.09800000 0.09700000 coda[1, , drop = FALSE] # Get the first row #> <CompositionMatrix: 1 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093  ## Subcomposition subcoda <- coda[, 1:3, drop = FALSE] # Get the first three column head(subcoda) #> <CompositionMatrix: 6 x 3> #>        A     B     C #> H1 0.488 0.317 0.038 #> H2 0.482 0.238 0.090 #> H3 0.370 0.091 0.342 #> H4 0.509 0.238 0.072 #> H5 0.442 0.383 0.029 #> H6 0.523 0.262 0.042"},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":null,"dir":"Reference","previous_headings":"","what":"Row Sums — totals","title":"Row Sums — totals","text":"Retrieves defines row sums (closure).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Row Sums — totals","text":"","code":"get_totals(x)  set_totals(x) <- value  # S4 method for CompositionMatrix get_totals(x)  # S4 method for CompositionMatrix set_totals(x) <- value"},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Row Sums — totals","text":"x object get set totals. value possible value totals x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Row Sums — totals","text":"set_totals() returns object sort x new row sums assigned. get_totals() returns row sums x.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Row Sums — totals","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Row Sums — totals","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies get_totals(B) #>  [1] 232 341 140 199 252 296 155 263 253 268 249 188 252 286 278 172 304 241 237 #> [20] 221  ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to a data.frame X <- data.frame(B) head(X) #>            V1         V2         V3        V4         V5 #> S1 0.07758621 0.01724138 0.40086207 0.1206897 0.38362069 #> S2 0.27859238 0.10557185 0.25219941 0.1612903 0.20234604 #> S3 0.33571429 0.26428571 0.02142857 0.3642857 0.01428571 #> S4 0.22110553 0.30150754 0.10552764 0.1206030 0.25125628 #> S5 0.32936508 0.13095238 0.11904762 0.1428571 0.27777778 #> S6 0.13851351 0.19932432 0.11824324 0.2162162 0.32770270"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":null,"dir":"Reference","previous_headings":"","what":"Additive Log-Ratios (ALR) — transform_alr","title":"Additive Log-Ratios (ALR) — transform_alr","text":"Computes ALR transformation.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Additive Log-Ratios (ALR) — transform_alr","text":"","code":"transform_alr(object, ...)  # S4 method for CompositionMatrix transform_alr(object, j = ncol(object))"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Additive Log-Ratios (ALR) — transform_alr","text":"object CompositionMatrix object. ... Currently used. j integer giving index rationing part (denominator).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Additive Log-Ratios (ALR) — transform_alr","text":"ALR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Additive Log-Ratios (ALR) — transform_alr","text":"ALR transformation logratio pair parts respect fixed part.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Additive Log-Ratios (ALR) — transform_alr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press. Greenacre, M. J. (2021). Compositional Data Analysis. Annual Review Statistics Application, 8(1): 271-299. doi:10.1146/annurev-statistics-042720-124436 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Additive Log-Ratios (ALR) — transform_alr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Additive Log-Ratios (ALR) — transform_alr","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":null,"dir":"Reference","previous_headings":"","what":"Centered Log-Ratios (CLR) — transform_clr","title":"Centered Log-Ratios (CLR) — transform_clr","text":"Computes CLR transformation.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Centered Log-Ratios (CLR) — transform_clr","text":"","code":"transform_clr(object, ...)  # S4 method for CompositionMatrix transform_clr(object, weights = FALSE)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Centered Log-Ratios (CLR) — transform_clr","text":"object CompositionMatrix object. ... Currently used. weights logical scalar: sould varying weight used. FALSE (default), equally-weighted parts used. Alternatively, positive numeric vector weights can specified.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Centered Log-Ratios (CLR) — transform_clr","text":"CLR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Centered Log-Ratios (CLR) — transform_clr","text":"CLR transformation computes log part relative geometric mean parts.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Centered Log-Ratios (CLR) — transform_clr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press. Greenacre, M. J. (2021). Compositional Data Analysis. Annual Review Statistics Application, 8(1): 271-299. doi:10.1146/annurev-statistics-042720-124436 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Centered Log-Ratios (CLR) — transform_clr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Centered Log-Ratios (CLR) — transform_clr","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":null,"dir":"Reference","previous_headings":"","what":"Isometric Log-Ratios (ILR) — transform_ilr","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"Computes ILR transformations.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"","code":"transform_ilr(object, ...)  # S4 method for CompositionMatrix transform_ilr(object)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"object CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"ILR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"ILR transformation provides coordinates composition respect given orthonormal basis. transform_ilr() uses orthonormal basis (Helmert matrix) originally defined Egozcue et al. (2003).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barceló-Vidal, C. (2003). Isometric Logratio Transformations Compositional Data Analysis. Mathematical Geology, 35(3), 279-300. doi:10.1023/:1023818214614 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press. Greenacre, M. J. (2021). Compositional Data Analysis. Annual Review Statistics Application, 8(1): 271-299. doi:10.1146/annurev-statistics-042720-124436 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse Log-Ratio Transformation — transform_inverse","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"Computes inverse log-ratio transformations.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"","code":"transform_inverse(object, origin, ...)  # S4 method for CLR,missing transform_inverse(object)  # S4 method for ALR,missing transform_inverse(object)  # S4 method for ILR,missing transform_inverse(object)  # S4 method for matrix,ILR transform_inverse(object, origin)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"object LogRatio object. origin LogRatio object used inverse transformation. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"CompositionMatrix object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barceló-Vidal, C. (2003). Isometric Logratio Transformations Compositional Data Analysis. Mathematical Geology, 35(3), 279-300. doi:10.1023/:1023818214614 . Fišerová, E. & Hron, K. (2011). Interpretation Orthonormal Coordinates Compositional Data. Mathematical Geosciences, 43(4), 455‑468. doi:10.1007/s11004-011-9333-x . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairwise Log-Ratios (LR) — transform_lr","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"Computes pairwise log-ratio transformation.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"","code":"transform_lr(object, ...)  # S4 method for CompositionMatrix transform_lr(object)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"object CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"LR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press. Greenacre, M. J. (2021). Compositional Data Analysis. Annual Review Statistics Application, 8(1): 271-299. doi:10.1146/annurev-statistics-042720-124436 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":null,"dir":"Reference","previous_headings":"","what":"Pivot Log-Ratios (PLR) — transform_plr","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"Computes PLR transformations.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"","code":"transform_plr(object, ...)  # S4 method for CompositionMatrix transform_plr(object, pivot = 1)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"object CompositionMatrix object. ... Currently used. pivot integer giving index pivotal variable.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"PLR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"Fišerová, E. & Hron, K. (2011). Interpretation Orthonormal Coordinates Compositional Data. Mathematical Geosciences, 43(4), 455‑468. doi:10.1007/s11004-011-9333-x . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press. Greenacre, M. J. (2021). Compositional Data Analysis. Annual Review Statistics Application, 8(1): 271-299. doi:10.1146/annurev-statistics-042720-124436 . Hron, K., Filzmoser, P., de Caritat, P., Fišerová, E. & Gardlo, . (2017). Weighted Pivot Coordinates Compositional Data Application Geochemical Mapping. Mathematical Geosciences, 49(6), 797-814. doi:10.1007/s11004-017-9684-z .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":null,"dir":"Reference","previous_headings":"","what":"Variation Matrix — variation","title":"Variation Matrix — variation","text":"Computes variation matrix (Aitchison 1986, definition 4.4).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variation Matrix — variation","text":"","code":"variation(x, ...)  # S4 method for CompositionMatrix variation(x)"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variation Matrix — variation","text":"x CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variation Matrix — variation","text":"matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Variation Matrix — variation","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Variation Matrix — variation","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variation Matrix — variation","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Variation matrix ## (Aitchison 1986, definition 4.4) (varia <- variation(coda)) #>            A         B         C          D         E #> A 0.00000000 0.2592742 1.5328586 0.08281464 0.1385604 #> B 0.25927416 0.0000000 3.0006804 0.54727146 0.6490135 #> C 1.53285862 3.0006804 0.0000000 1.11145146 0.9476375 #> D 0.08281464 0.5472715 1.1114515 0.00000000 0.1870579 #> E 0.13856035 0.6490135 0.9476375 0.18705791 0.0000000  ## Cluster dendrogram d <- as.dist(varia) h <- hclust(d, method = \"ward.D2\") plot(h)"}]
+[{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"get-started","dir":"Articles","previous_headings":"","what":"Get started","title":"Introduction to nexus","text":"nexus provides set S4 classes represent different special types matrix. basic class represents compositional data matrix, .e. quantitative (positive) descriptions parts whole, carrying relative, rather absolute, information (Aitchison 1986; Greenacre 2021). assumes keep data tidy: variable must saved column observation (sample) must saved row. class simple use inherits base matrix: CompositionMatrix represents closed composition matrix: row matrix sum 1 (relative changes relevant compositional data analysis). original row sums kept internally, source data can restored:","code":"## Install extra packages (if needed): # install.packages(\"folio\")  library(nexus) ## Mineral compositions of rock specimens ## Data from Aitchison 1986 data(\"hongite\") head(hongite) #>       A    B    C    D    E #> H1 48.8 31.7  3.8  6.4  9.3 #> H2 48.2 23.8  9.0  9.2  9.8 #> H3 37.0  9.1 34.2  9.5 10.2 #> H4 50.9 23.8  7.2 10.1  8.0 #> H5 44.2 38.3  2.9  7.7  6.9 #> H6 52.3 26.2  4.2 12.5  4.8  ## Coerce to compositional data coda <- as_composition(hongite) head(coda) #> <CompositionMatrix: 6 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093 #> H2 0.482 0.238 0.090 0.092 0.098 #> H3 0.370 0.091 0.342 0.095 0.102 #> H4 0.509 0.238 0.072 0.101 0.080 #> H5 0.442 0.383 0.029 0.077 0.069 #> H6 0.523 0.262 0.042 0.125 0.048 ## Coerce to count data counts <- as_amounts(coda)  all.equal(hongite, as.data.frame(counts)) #> [1] TRUE ## Ternary plots plot(coda) ## Compositional bar plot barplot(coda, order = \"A\")"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"working-with-reference-groups","dir":"Articles","previous_headings":"Get started","what":"Working with (reference) groups","title":"Introduction to nexus","text":"Provenance studies typically rely two approaches, can used together: Identification groups among artifacts studied, based mineralogical geochemical criteria (clustering). Comparison -called reference groups, .e. known geological sources productive contexts (classification). nexus allows specify whether observation belongs specific group (). coercing data.frame CompositionMatrix object, attempt made automatically detect groups mapping column names. behavior can disabled setting options(nexus.autodetect = FALSE) overridden explicitly specifying column used groups argument as_composition(). get_groups(x) set_groups(x) <- value allow retrieve set groups existing CompositionMatrix (NA can used specify sample belong group): groups defined, can used methods (e.g. plotting).","code":"## Create a data.frame X <- data.frame(   groups = c(\"A\", \"A\", \"B\", \"A\", \"B\", \"C\", \"C\", \"C\", \"B\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X) any_assigned(Y) #> [1] TRUE ## Set groups (NA means no group) set_groups(Y) <- c(\"X\", \"X\", \"Y\", \"X\", \"Y\", NA, NA, NA, \"Y\")  ## Retrieve groups get_groups(Y) #> [1] \"X\" \"X\" \"Y\" \"X\" \"Y\" NA  NA  NA  \"Y\""},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"working-with-repeated-measurements","dir":"Articles","previous_headings":"Get started","what":"Working with repeated measurements","title":"Introduction to nexus","text":"situations, measurements may repeated (e.g. multiple chemical analyses sample). presence repeated measurements can specified giving several observations sample name. coercing data.frame CompositionMatrix object, attempt made automatically detect samples mapping column names. matching column found, row names used default. behavior can disabled setting options(nexus.autodetect = FALSE) overridden explicitly specifying column used samples argument as_composition(). get_samples(x) set_samples(x) <- value allow retrieve set sample names existing CompositionMatrix (missing values allowed): Note presence repeated measurements may affect calculations (read documentation carefully).","code":"## Create a data.frame X <- data.frame(   samples = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"C\", \"C\", \"C\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X) any_replicated(Y) #> [1] TRUE ## Set sample names set_samples(Y) <- c(\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\")  ## Retrieve groups get_samples(Y) #> [1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\" \"G\" \"H\" \"I\""},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"log-ratio-transformations","dir":"Articles","previous_headings":"","what":"Log-ratio transformations","title":"Introduction to nexus","text":"package provides following transformations: centered log ratio (CLR, Aitchison 1986), additive log ratio (ALR, Aitchison 1986), isometric log ratio (ILR, Egozcue et al. 2003) pivot log-ratio (PLR, Hron et al. 2017).","code":"## CLR clr <- transform_clr(coda) head(clr) #>            A          B          C           D          E #> H1 1.3346582  0.9032446 -1.2180710 -0.69677410 -0.3230577 #> H2 1.1265880  0.4209146 -0.5515464 -0.52956753 -0.4663886 #> H3 0.8258984 -0.5767451  0.7472061 -0.53372770 -0.4626318 #> H4 1.2367416  0.4765643 -0.7190403 -0.38058588 -0.6136798 #> H5 1.2943994  1.1511245 -1.4296147 -0.45310510 -0.5628040 #> H6 1.4065594  0.7153224 -1.1153524 -0.02470833 -0.9818211  plot(clr) back <- transform_inverse(clr) head(back) #> <CompositionMatrix: 6 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093 #> H2 0.482 0.238 0.090 0.092 0.098 #> H3 0.370 0.091 0.342 0.095 0.102 #> H4 0.509 0.238 0.072 0.101 0.080 #> H5 0.442 0.383 0.029 0.077 0.069 #> H6 0.523 0.262 0.042 0.125 0.048"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"multivariate-methods","dir":"Articles","previous_headings":"","what":"Multivariate methods","title":"Introduction to nexus","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\")  ## Remove rows with missing values kommos <- remove_NA(kommos, margin = 1)  ## Coerce to a compositional matrix coda <- as_composition(kommos) #> 2 qualitative variables were removed: type, date.  ## Set groups set_groups(coda) <- kommos$type ## Compositional bar plot barplot(coda, order = \"Ca\")"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"principle-component-analysis","dir":"Articles","previous_headings":"Multivariate methods","what":"Principle Component Analysis","title":"Introduction to nexus","text":"","code":"## CLR clr <- transform_clr(coda)  ## PCA library(dimensio)  clr_pca <- pca(clr, scale = FALSE)  viz_individuals(clr_pca, highlight = get_groups(coda), pch = 16,                  col = c(\"#EE7733\", \"#0077BB\", \"#33BBEE\", \"#EE3377\")) viz_variables(clr_pca)"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"manova","dir":"Articles","previous_headings":"Multivariate methods","what":"MANOVA","title":"Introduction to nexus","text":"MANOVA results suggest statistically significant differences groups.","code":"## ILR ilr <- transform_ilr(coda)  ## MANOVA fit <- manova(ilr ~ get_groups(ilr)) summary(fit) #>                 Df Pillai approx F num Df den Df    Pr(>F)     #> get_groups(ilr)  3 1.7932   4.8487     57    186 < 2.2e-16 *** #> Residuals       78                                             #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"discriminant-analysis","dir":"Articles","previous_headings":"Multivariate methods","what":"Discriminant Analysis","title":"Introduction to nexus","text":"","code":"## LDA discr <- MASS::lda(ilr, grouping = get_groups(ilr)) plot(discr) ## Back transform results transform_inverse(discr$means, origin = ilr) #>              Sm           Lu           U          Yb         As           Sb #> CJ  0.014938709 0.0008558484 0.007253021 0.006793156 0.02199766 0.0014569243 #> EJ  0.018613143 0.0010139916 0.007584757 0.008127263 0.01819848 0.0012892835 #> SNA 0.007141080 0.0005084305 0.003701554 0.003950209 0.01127728 0.0009184591 #> TSJ 0.009835545 0.0006601008 0.005386968 0.005152492 0.01369280 0.0018069781 #>              Ca           Na         La         Ce         Th        Cr #> CJ  0.029204741 0.0009599806 0.07678692 0.16797505 0.01897389 0.4392215 #> EJ  0.025610707 0.0018649908 0.09678585 0.21657222 0.02321610 0.3286455 #> SNA 0.007737157 0.0012335187 0.03893221 0.08823033 0.01467079 0.6248961 #> TSJ 0.008745270 0.0013825884 0.05646163 0.11939198 0.01937137 0.4647827 #>              Hf          Cs         Sc         Rb          Fe          Ta #> CJ  0.011779918 0.004874522 0.03227958 0.10114053 0.010336253 0.003030762 #> EJ  0.017996210 0.004785199 0.04344994 0.10870167 0.013130417 0.004482816 #> SNA 0.005908416 0.004618774 0.03019832 0.09111503 0.008644199 0.001635972 #> TSJ 0.008497490 0.011135602 0.03551225 0.17345781 0.010099512 0.002099366 #>             Co          Eu #> CJ  0.04679035 0.003350694 #> EJ  0.05566413 0.004267303 #> SNA 0.05301923 0.001662958 #> TSJ 0.05036299 0.002164612"},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/nexus/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Sourcing Archaeological Materials by Chemical Composition","text":"Exploration analysis compositional data framework Aitchison (1986). nexus provides tools chemical fingerprinting source tracking ancient materials. package provides methods compositional data analysis: Logratio transformations: transform_lr(), transform_clr(), transform_alr(), transform_ilr(), transform_plr(). Compositional statistics. Zero missing value replacement. Outlier detection: outliers(). package also includes methods provenance studies: Multivariate analysis: pca(). Mixed-mode analysis using geochemical petrographic data (Baxter et al. 2008): mix(). isopleuros companion package nexus allows create ternary plots.","code":"To cite nexus in publications use:    Frerebeau N, Philippe A (2023). _nexus: Sourcing Archaeological   Materials by Chemical Composition_. Université Bordeaux Montaigne,   Pessac, France. R package version 0.1.0,   <https://packages.tesselle.org/nexus/>.  A BibTeX entry for LaTeX users is    @Manual{,     author = {Nicolas Frerebeau and Anne Philippe},     title = {{nexus: Sourcing Archaeological Materials by Chemical Composition}},     year = {2023},     organization = {Université Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 0.1.0},     url = {https://packages.tesselle.org/nexus/},   }  This package is a part of the tesselle project <https://www.tesselle.org>."},{"path":"https://packages.tesselle.org/nexus/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Sourcing Archaeological Materials by Chemical Composition","text":"can install released version nexus CRAN : development version GitHub :","code":"install.packages(\"nexus\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/nexus\")"},{"path":"https://packages.tesselle.org/nexus/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Sourcing Archaeological Materials by Chemical Composition","text":"nexus provides set S4 classes represent different special types matrix. basic class represents compositional data matrix, .e. quantitative (nonnegative) descriptions parts whole, carrying relative, rather absolute, information (Aitchison 1986). assumes keep data tidy: variable must saved column observation (sample) must saved row. new classes simple use inherit base matrix (see vignette(\"nexus\")): nexus allows specify whether observation belongs specific group (). Additionally, presence repeated measurements can specified giving several observations sample name:","code":"## Load the package library(nexus) ## Mineral compositions of rock specimens ## Data from Aitchison 1986 data(\"hongite\") head(hongite) #>       A    B    C    D    E #> H1 48.8 31.7  3.8  6.4  9.3 #> H2 48.2 23.8  9.0  9.2  9.8 #> H3 37.0  9.1 34.2  9.5 10.2 #> H4 50.9 23.8  7.2 10.1  8.0 #> H5 44.2 38.3  2.9  7.7  6.9 #> H6 52.3 26.2  4.2 12.5  4.8  ## Coerce to compositional data coda <- as_composition(hongite) head(coda) #> <CompositionMatrix: 6 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093 #> H2 0.482 0.238 0.090 0.092 0.098 #> H3 0.370 0.091 0.342 0.095 0.102 #> H4 0.509 0.238 0.072 0.101 0.080 #> H5 0.442 0.383 0.029 0.077 0.069 #> H6 0.523 0.262 0.042 0.125 0.048 ## Mineral compositions of five slides as reported by five analysts ## Data from Aitchison 1986 data(\"slides\") head(slides) #>   analyst slide quartz microcline plagioclass biotite muscovite opaques #> 1      A1     A   24.7       35.6        33.3     3.3       2.0     0.6 #> 2      A1     B   26.8       35.7        32.6     3.5       0.4     0.6 #> 3      A1     C   28.0       34.2        32.1     3.4       1.1     0.7 #> 4      A1     D   27.8       35.0        31.5     3.3       1.0     0.9 #> 5      A1     E   26.6       34.5        33.6     3.0       1.4     0.6 #> 6      A2     A   27.3       35.5        32.1     2.5       1.5     0.8 #>   nonopaques #> 1        0.6 #> 2        0.4 #> 3        0.4 #> 4        0.5 #> 5        0.3 #> 6        0.3  ## Coerce to compositional data coda <- as_composition(slides, sample = 2, group = 1) head(coda) #> <CompositionMatrix: 6 x 7> #>      quartz microcline plagioclass    biotite  muscovite     opaques #> 1 0.2467532  0.3556444   0.3326673 0.03296703 0.01998002 0.005994006 #> 2 0.2680000  0.3570000   0.3260000 0.03500000 0.00400000 0.006000000 #> 3 0.2802803  0.3423423   0.3213213 0.03403403 0.01101101 0.007007007 #> 4 0.2780000  0.3500000   0.3150000 0.03300000 0.01000000 0.009000000 #> 5 0.2660000  0.3450000   0.3360000 0.03000000 0.01400000 0.006000000 #> 6 0.2730000  0.3550000   0.3210000 0.02500000 0.01500000 0.008000000 #>    nonopaques #> 1 0.005994006 #> 2 0.004000000 #> 3 0.004004004 #> 4 0.005000000 #> 5 0.003000000 #> 6 0.003000000"},{"path":"https://packages.tesselle.org/nexus/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Sourcing Archaeological Materials by Chemical Composition","text":"Please note nexus project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Compositional Matrix — CompositionMatrix-class","title":"Compositional Matrix — CompositionMatrix-class","text":"S4 class represent compositional data.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Compositional Matrix — CompositionMatrix-class","text":"totals numeric vector store absolute row sums (closure compositions). codes character vector store laboratory codes (unique identifiers). samples character vector store sample identifiers (allows duplicates case repeated measurements). groups character vector store group names ().","code":""},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Compositional Matrix — CompositionMatrix-class","text":"class inherits matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Compositional Matrix — CompositionMatrix-class","text":"code snippets , x CompositionMatrix object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compositional Matrix — CompositionMatrix-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compositional Matrix — CompositionMatrix-class","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## codaccess dim(coda) # Get the matrix dimensions #> [1] 25  5 row(coda) # Get the row indexes #>       [,1] [,2] [,3] [,4] [,5] #>  [1,]    1    1    1    1    1 #>  [2,]    2    2    2    2    2 #>  [3,]    3    3    3    3    3 #>  [4,]    4    4    4    4    4 #>  [5,]    5    5    5    5    5 #>  [6,]    6    6    6    6    6 #>  [7,]    7    7    7    7    7 #>  [8,]    8    8    8    8    8 #>  [9,]    9    9    9    9    9 #> [10,]   10   10   10   10   10 #> [11,]   11   11   11   11   11 #> [12,]   12   12   12   12   12 #> [13,]   13   13   13   13   13 #> [14,]   14   14   14   14   14 #> [15,]   15   15   15   15   15 #> [16,]   16   16   16   16   16 #> [17,]   17   17   17   17   17 #> [18,]   18   18   18   18   18 #> [19,]   19   19   19   19   19 #> [20,]   20   20   20   20   20 #> [21,]   21   21   21   21   21 #> [22,]   22   22   22   22   22 #> [23,]   23   23   23   23   23 #> [24,]   24   24   24   24   24 #> [25,]   25   25   25   25   25 col(coda, as.factor = TRUE) # Get the column indexes #>       [,1] [,2] [,3] [,4] [,5] #>  [1,] A    B    C    D    E    #>  [2,] A    B    C    D    E    #>  [3,] A    B    C    D    E    #>  [4,] A    B    C    D    E    #>  [5,] A    B    C    D    E    #>  [6,] A    B    C    D    E    #>  [7,] A    B    C    D    E    #>  [8,] A    B    C    D    E    #>  [9,] A    B    C    D    E    #> [10,] A    B    C    D    E    #> [11,] A    B    C    D    E    #> [12,] A    B    C    D    E    #> [13,] A    B    C    D    E    #> [14,] A    B    C    D    E    #> [15,] A    B    C    D    E    #> [16,] A    B    C    D    E    #> [17,] A    B    C    D    E    #> [18,] A    B    C    D    E    #> [19,] A    B    C    D    E    #> [20,] A    B    C    D    E    #> [21,] A    B    C    D    E    #> [22,] A    B    C    D    E    #> [23,] A    B    C    D    E    #> [24,] A    B    C    D    E    #> [25,] A    B    C    D    E    #> Levels: A B C D E nrow(coda) # Get the number of rows #> [1] 25 ncol(coda) # Get the number of columns #> [1] 5 dimnames(coda) # Get the dimension names #> [[1]] #>  [1] \"H1\"  \"H2\"  \"H3\"  \"H4\"  \"H5\"  \"H6\"  \"H7\"  \"H8\"  \"H9\"  \"H10\" \"H11\" \"H12\" #> [13] \"H13\" \"H14\" \"H15\" \"H16\" \"H17\" \"H18\" \"H19\" \"H20\" \"H21\" \"H22\" \"H23\" \"H24\" #> [25] \"H25\" #>  #> [[2]] #> [1] \"A\" \"B\" \"C\" \"D\" \"E\" #>  rownames(coda) <- LETTERS[1:25] # Set the row names rownames(coda) # Get the rownames #>  [1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\" \"G\" \"H\" \"I\" \"J\" \"K\" \"L\" \"M\" \"N\" \"O\" \"P\" \"Q\" \"R\" \"S\" #> [20] \"T\" \"U\" \"V\" \"W\" \"X\" \"Y\" colnames(coda) <- letters[21:25] # Set the column names colnames(coda) # Get the column names #> [1] \"u\" \"v\" \"w\" \"x\" \"y\""},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Log-Ratio Matrix — LogRatio-class","title":"Log-Ratio Matrix — LogRatio-class","text":"S4 classes represent log-ratio data transformations.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Log-Ratio Matrix — LogRatio-class","text":"totals numeric vector store absolute row sums (closure compositions). codes character vector store laboratory codes (unique identifiers). samples character vector store sample identifiers (allows duplicates case repeated measurements). groups character vector store group names (). parts character vector store part names. ratio character vector store ratio names. order integer vector store original ordering columns. base numeric matrix store basis transformation. weights numeric vector store weights assigned respective log-ratios.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Log-Ratio Matrix — LogRatio-class","text":"classes inherit matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Log-Ratio Matrix — LogRatio-class","text":"code snippets , x LogRatio object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Log-Ratio Matrix — LogRatio-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical Matrix — LogicalMatrix-class","title":"Logical Matrix — LogicalMatrix-class","text":"S4 classes represent \\(m \\times p\\) logical matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Logical Matrix — LogicalMatrix-class","text":".Data \\(m \\times p\\) logical matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Logical Matrix — LogicalMatrix-class","text":"class inherits matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Logical Matrix — LogicalMatrix-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Numeric Matrix — NumericMatrix-class","title":"Numeric Matrix — NumericMatrix-class","text":"S4 classes represent \\(m \\times p\\) numeric matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Numeric Matrix — NumericMatrix-class","text":".Data \\(m \\times p\\) numeric matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Numeric Matrix — NumericMatrix-class","text":"class inherits matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Numeric Matrix — NumericMatrix-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Outliers — OutlierIndex-class","title":"Outliers — OutlierIndex-class","text":"S4 class store result outlier detection.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Outliers — OutlierIndex-class","text":".Data logical matrix. codes character vector store laboratory codes (unique identifiers). samples character vector store sample identifiers (allows duplicates case repeated measurements). groups character vector store group names (). distances numeric matrix giving squared Mahalanobis distance. limit numeric value giving cut-value used outlier detection (quantile Chi-squared distribution). robust logical scalar: robust estimators used? dof (non-negative) numeric value giving degrees freedom.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Outliers — OutlierIndex-class","text":"classes inherit logical.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Outliers — OutlierIndex-class","text":"code snippets , x OutlierIndex object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Outliers — OutlierIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Summary Statistics of Data Subsets — aggregate","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"Splits data subsets, computes summary statistics , returns result.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"","code":"# S4 method for CompositionMatrix aggregate(x, by, FUN, ...)"},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"x CompositionMatrix object. character string specifying grouping element. must one \"samples\" \"groups\". unambiguous substring can given. FUN function compute summary statistics. ... arguments passed FUN.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Summary Statistics of Data Subsets — aggregate","text":"","code":"## Create a data.frame X <- data.frame(   samples = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"C\", \"C\", \"C\"),   groups = c(\"X\", \"X\", \"X\", \"X\", \"X\", \"X\", \"Y\", \"Y\", \"Y\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X)  ## Compositional mean by sample aggregate(Y, by = \"samples\", FUN = mean) #>          Ca        Fe         Na #> A 0.4468157 0.4539051 0.09927924 #> B 0.4746506 0.4742013 0.05114805 #> C 0.3067545 0.6238351 0.06941036  ## Metric variance by group aggregate(Y, by = \"groups\", FUN = metric_var) #>        [,1] #> X 0.2127884 #> Y 0.4275836"},{"path":"https://packages.tesselle.org/nexus/reference/arctic.html","id":null,"dir":"Reference","previous_headings":"","what":"Arctic Lake — arctic","title":"Arctic Lake — arctic","text":"Sand, silt, clay compositions 39 sediment samples different water depths Arctic lake.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arctic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arctic Lake — arctic","text":"","code":"arctic"},{"path":"https://packages.tesselle.org/nexus/reference/arctic.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Arctic Lake — arctic","text":"data.frame 4 variables: sand Sand content (percent). silt Silt content (percent). clay Clay content (percent). depth Water depth (m).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arctic.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Arctic Lake — arctic","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":null,"dir":"Reference","previous_headings":"","what":"Operations in the Simplex — arithmetic","title":"Operations in the Simplex — arithmetic","text":"Operators performing operations simplex.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Operations in the Simplex — arithmetic","text":"","code":"x %perturbe% y  x %power% y  # S4 method for CompositionMatrix,CompositionMatrix %perturbe%(x, y)  # S4 method for CompositionMatrix,numeric %power%(x, y)  # S4 method for numeric,CompositionMatrix %power%(x, y)"},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Operations in the Simplex — arithmetic","text":"x CompositionMatrix object. y CompositionMatrix object numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Operations in the Simplex — arithmetic","text":"CompositionMatrix object numeric vector (x).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Operations in the Simplex — arithmetic","text":"%perturbe% Perturbation operation. %power% Powering operation.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Operations in the Simplex — arithmetic","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/arithmetic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Operations in the Simplex — arithmetic","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #>           V1        V2   V3 #> S1 0.4166667 0.8333333 0.75  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>     V1 V2  V3 #> S1 0.5  1 0.5  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to Amounts — as_amounts","title":"Coerce to Amounts — as_amounts","text":"Coerce Amounts","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to Amounts — as_amounts","text":"","code":"as_amounts(from, ...)  # S4 method for CompositionMatrix as_amounts(from)"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to Amounts — as_amounts","text":"CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to Amounts — as_amounts","text":"numeric  matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to Amounts — as_amounts","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to Amounts — as_amounts","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies get_totals(B) #>  [1] 245 201 256 241 152 177 438 248 237 356 317 190 261 265 255 277 301 273 251 #> [20] 278  ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to a data.frame X <- data.frame(B) head(X) #>             V1         V2         V3        V4        V5 #> S1 0.289795918 0.07346939 0.17142857 0.2204082 0.2448980 #> S2 0.243781095 0.16417910 0.06467662 0.2985075 0.2288557 #> S3 0.210937500 0.17187500 0.10546875 0.3554688 0.1562500 #> S4 0.004149378 0.26970954 0.29460581 0.2655602 0.1659751 #> S5 0.105263158 0.32894737 0.17763158 0.1710526 0.2171053 #> S6 0.028248588 0.16384181 0.32203390 0.2824859 0.2033898"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to a Closed Compositional Matrix — as_composition","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"Coerces object CompositionMatrix object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"","code":"as_composition(from, ...)  # S4 method for numeric as_composition(from)  # S4 method for matrix as_composition(from)  # S4 method for data.frame as_composition(   from,   codes = NULL,   samples = NULL,   groups = NULL,   auto = getOption(\"nexus.autodetect\"),   verbose = getOption(\"nexus.verbose\") )"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"matrix data.frame coerced. ... Currently used. codes integer giving index column used laboratory codes (unique identifiers). samples integer giving index column used sample identification: allows duplicates identify replicated measurements. NULL (default), row names used sample IDs. groups integer giving index column used group samples. NULL (default), grouping stored. auto logical scalar: try automatically detect codes, samples groups columns? verbose logical scalar: R report extra information progress?","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"CompositionMatrix object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"CompositionMatrix class special slots: codes laboratory codes, samples repeated measurements/observation, groups group data site/area. coercing data.frame CompositionMatrix object, attempt made automatically assign values slots mapping column names (case insensitive, plural insensitive). behavior can disabled setting options(nexus.autodetect = FALSE) overridden explicitly specifying columns used. See vignette(\"nexus\").","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"non-numeric variable removed.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to a Closed Compositional Matrix — as_composition","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies get_totals(B) #>  [1] 167 276 214 286 273 251 293 234 285 324 323 311 259 233 249 232 220 223 177 #> [20] 323  ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to a data.frame X <- data.frame(B) head(X) #>           V1        V2          V3         V4         V5 #> S1 0.4610778 0.1736527 0.077844311 0.06586826 0.22155689 #> S2 0.3586957 0.1123188 0.003623188 0.35507246 0.17028986 #> S3 0.1121495 0.2336449 0.280373832 0.35981308 0.01401869 #> S4 0.2482517 0.1293706 0.290209790 0.27622378 0.05594406 #> S5 0.1062271 0.1978022 0.124542125 0.20879121 0.36263736 #> S6 0.2509960 0.3426295 0.103585657 0.08366534 0.21912351"},{"path":"https://packages.tesselle.org/nexus/reference/as_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to Features — as_features","title":"Coerce to Features — as_features","text":"Converts object collection features.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to Features — as_features","text":"","code":"as_features(from, ...)  # S4 method for CompositionMatrix as_features(from)"},{"path":"https://packages.tesselle.org/nexus/reference/as_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to Features — as_features","text":"CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to Features — as_features","text":"data.frame informations extra columns.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/as_features.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to Features — as_features","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_features.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to Features — as_features","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies get_totals(B) #>  [1] 337 321 154 216 134 315 328 188 373 227 248 231 184 108 368 271 254 273 352 #> [20] 130  ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to a data.frame X <- data.frame(B) head(X) #>           V1         V2         V3         V4         V5 #> S1 0.2759644 0.08902077 0.11572700 0.23145401 0.28783383 #> S2 0.3084112 0.13707165 0.29283489 0.01246106 0.24922118 #> S3 0.1428571 0.22727273 0.14285714 0.44155844 0.04545455 #> S4 0.0462963 0.28240741 0.45370370 0.09259259 0.12500000 #> S5 0.1791045 0.20895522 0.01492537 0.56716418 0.02985075 #> S6 0.2793651 0.29206349 0.25714286 0.09841270 0.07301587"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Graph of Log-ratios — as_graph","title":"Graph of Log-ratios — as_graph","text":"Produces graph log-ratios.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graph of Log-ratios — as_graph","text":"","code":"as_graph(object, ...)  # S4 method for LR as_graph(object)  # S4 method for ALR as_graph(object)  # S4 method for ILR as_graph(object)"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Graph of Log-ratios — as_graph","text":"object LogRatio object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Graph of Log-ratios — as_graph","text":"igraph graph object.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Graph of Log-ratios — as_graph","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graph of Log-ratios — as_graph","text":"","code":"library(igraph) #>  #> Attaching package: ‘igraph’ #> The following objects are masked from ‘package:stats’: #>  #>     decompose, spectrum #> The following object is masked from ‘package:base’: #>  #>     union  ## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda) lr_graph <- as_graph(lr) plot(lr_graph)   ## Additive log-ratio alr <- transform_alr(coda) alr_graph <- as_graph(alr) plot(alr_graph)   ## Isometric log-ratio ilr <- transform_ilr(coda) ilr_graph <- as_graph(ilr) plot(ilr_graph)   plr <- transform_plr(coda) plr_graph <- as_graph(plr) plot(plr_graph)"},{"path":"https://packages.tesselle.org/nexus/reference/barplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Barplot of Compositional Data — barplot","title":"Barplot of Compositional Data — barplot","text":"Displays compositional bar chart.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/barplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Barplot of Compositional Data — barplot","text":"","code":"# S4 method for CompositionMatrix barplot(   height,   ...,   order = NULL,   decreasing = FALSE,   groups = get_groups(height),   horiz = TRUE,   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE )"},{"path":"https://packages.tesselle.org/nexus/reference/barplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Barplot of Compositional Data — barplot","text":"height CompositionMatrix object. ... parameters passed graphics::barplot(). order integer vector giving index column used ordering data. decreasing logical scalar: sort order increasing decreasing? groups factor sense .factor(groups) defines grouping. set, matrix panels defined groups drawn. horiz logical scalar. FALSE, bars drawn vertically first bar left. TRUE (default), bars drawn horizontally first bottom. xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot?","code":""},{"path":"https://packages.tesselle.org/nexus/reference/barplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Barplot of Compositional Data — barplot","text":"barplot() called side-effects: results graphic displayed (invisibly return height).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/barplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Barplot of Compositional Data — barplot","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/barplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Barplot of Compositional Data — barplot","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Bar plot barplot(coda, order = 2)   ## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  barplot(coda, order = 1, main = \"Kommos Ceramics\")  barplot(coda, order = 1, horiz = FALSE, main = \"Kommos Ceramics\")"},{"path":"https://packages.tesselle.org/nexus/reference/chemistry.html","id":null,"dir":"Reference","previous_headings":"","what":"Can Sora Chemical Data — chemistry","title":"Can Sora Chemical Data — chemistry","text":"Can Sora Chemical Data","code":""},{"path":"https://packages.tesselle.org/nexus/reference/chemistry.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Can Sora Chemical Data — chemistry","text":"","code":"chemistry"},{"path":"https://packages.tesselle.org/nexus/reference/chemistry.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Can Sora Chemical Data — chemistry","text":"data.frame 30 variables.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/chemistry.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Can Sora Chemical Data — chemistry","text":"Cau, M.-. (1999). Importaciones de cerámica tardorromana de cocina en las Iles Balears: el caso de Can Sora (Eivissa). J. Capel Martinez, Arqueometria y Arqueologia, p. 197-219. Granada: Editorial Universidad de Granada. Monografica Arte y Arqueología 47.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":null,"dir":"Reference","previous_headings":"","what":"Closure Operation — closure","title":"Closure Operation — closure","text":"Closes compositions sum 1.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Closure Operation — closure","text":"","code":"closure(x, ...)  # S4 method for numeric closure(x, total = 1, na.rm = FALSE)  # S4 method for matrix closure(x, total = 1, na.rm = FALSE)"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Closure Operation — closure","text":"x numeric vector matrix. ... Currently used. total numeric vector specifying total amount compositions closed (defaults 1). na.rm logical scalar: missing values removed?","code":""},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Closure Operation — closure","text":"numeric vector matrix (x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Closure Operation — closure","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Closure Operation — closure","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #>           V1        V2   V3 #> S1 0.4166667 0.8333333 0.75  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>     V1 V2  V3 #> S1 0.5  1 0.5  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":null,"dir":"Reference","previous_headings":"","what":"Covariance Matrix — covariance","title":"Covariance Matrix — covariance","text":"Computes (centered) log-ratio covariance matrix (see ).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Covariance Matrix — covariance","text":"","code":"covariance(x, ...)  # S4 method for CompositionMatrix covariance(x, center = TRUE, method = \"pearson\")  # S4 method for ALR covariance(x, method = \"pearson\")  # S4 method for CLR covariance(x, method = \"pearson\")"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Covariance Matrix — covariance","text":"x CompositionMatrix object. ... Currently used. center logical scalar: centered log-ratio covariance matrix computed? method character string indicating covariance computed (see stats::cov()).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Covariance Matrix — covariance","text":"matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"methods-by-class-","dir":"Reference","previous_headings":"","what":"Methods (by class)","title":"Covariance Matrix — covariance","text":"covariance(ALR): Computes log-ratio covariance matrix (Aitchison 1986, definition 4.5). covariance(CLR): Computes centered log-ratio covariance matrix (Aitchison 1986, definition 4.6).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Covariance Matrix — covariance","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Covariance Matrix — covariance","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Covariance Matrix — covariance","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Log-ratio covariance matrix ## (Aitchison 1986, definition 4.5) covariance(coda, center = FALSE) #>            A_E        B_E        C_E       D_E #> A_E  0.1385604  0.2641498 -0.2233304 0.1214018 #> B_E  0.2641498  0.6490135 -0.7020147 0.1444000 #> C_E -0.2233304 -0.7020147  0.9476375 0.0116220 #> D_E  0.1214018  0.1444000  0.0116220 0.1870579  ## Centered log-ratio covariance matrix ## (Aitchison 1986, definition 4.6) covariance(coda, center = TRUE) #>             A           B           C           D           E #> A  0.06443676  0.17907284 -0.24408053  0.01453821 -0.01396727 #> B  0.17907284  0.55298309 -0.73371823  0.02658296 -0.02492066 #> C -0.24408053 -0.73371823  0.98026080 -0.04186819  0.03940616 #> D  0.01453821  0.02658296 -0.04186819  0.04745430 -0.04670728 #> E -0.01396727 -0.02492066  0.03940616 -0.04670728  0.04618906"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Distances — dist","title":"Distances — dist","text":"Computes log-ratio variance matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distances — dist","text":"","code":"# S4 method for CompositionMatrix dist(x, method = \"euclidean\", diag = FALSE, upper = FALSE, p = 2)"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distances — dist","text":"x CompositionMatrix object. method character string specifying distance measure used. See stats::dist() available distances. diag logical scalar indicating whether diagonal distance matrix printed. upper logical scalar indicating whether upper triangle distance matrix printed. p integer giving power Minkowski distance.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distances — dist","text":"stats::dist object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distances — dist","text":"Distances computed CLR-transformed data.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Distances — dist","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distances — dist","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distances — dist","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Aitchison distance ## (euclidean distance between CLR-transformed compositions) d <- dist(coda)  ## Cluster dendrogram h <- hclust(d, method = \"ward.D2\") plot(h)"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Working With Groups — groups","title":"Working With Groups — groups","text":"Retrieves defines groups observations belong.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Working With Groups — groups","text":"","code":"any_assigned(x)  is_assigned(x)  get_groups(x)  set_groups(x) <- value  # S4 method for CompositionMatrix is_assigned(x)  # S4 method for LogRatio is_assigned(x)  # S4 method for OutlierIndex is_assigned(x)  # S4 method for CompositionMatrix any_assigned(x)  # S4 method for LogRatio any_assigned(x)  # S4 method for OutlierIndex any_assigned(x)  # S4 method for CompositionMatrix get_groups(x)  # S4 method for LogRatio get_groups(x)  # S4 method for OutlierIndex get_groups(x)  # S4 method for CompositionMatrix set_groups(x) <- value"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Working With Groups — groups","text":"x object get set groups. value possible value groups x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Working With Groups — groups","text":"set_groups() returns object sort x new group names assigned. get_groups() returns character vector giving group names x. any_assigned() returns logical scalar specifying whether x groups. is_assigned() returns logical vector specifying whether observation belongs group.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Working With Groups — groups","text":"See vignette(\"nexus\").","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Working With Groups — groups","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":null,"dir":"Reference","previous_headings":"","what":"Hongite Mineralogy — hongite","title":"Hongite Mineralogy — hongite","text":"Mineral compositions 25 rock specimens hongite type.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hongite Mineralogy — hongite","text":"","code":"hongite"},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Hongite Mineralogy — hongite","text":"data.frame 5 variables (minerals): Albite (percent). B Blandite (percent). C Cornite (percent). D Daubite (percent). E Endite (percent).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hongite Mineralogy — hongite","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/identifiers.html","id":null,"dir":"Reference","previous_headings":"","what":"Unique Identifiers — identifiers","title":"Unique Identifiers — identifiers","text":"Retrieves defines unique identifier (eg. laboratory codes) observation.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/identifiers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unique Identifiers — identifiers","text":"","code":"get_identifiers(x)  set_identifiers(x) <- value  # S4 method for CompositionMatrix get_identifiers(x)  # S4 method for LogRatio get_identifiers(x)  # S4 method for OutlierIndex get_identifiers(x)  # S4 method for CompositionMatrix set_identifiers(x) <- value"},{"path":"https://packages.tesselle.org/nexus/reference/identifiers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unique Identifiers — identifiers","text":"x object get set codes. value possible value codes x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/identifiers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unique Identifiers — identifiers","text":"set_identifiers() returns object sort x new identifiers assigned. get_identifiers() returns character vector giving unique identifiers x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/identifiers.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Unique Identifiers — identifiers","text":"See vignette(\"nexus\").","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/identifiers.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Unique Identifiers — identifiers","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":null,"dir":"Reference","previous_headings":"","what":"Mahalanobis Distance — mahalanobis","title":"Mahalanobis Distance — mahalanobis","text":"Computes squared Mahalanobis distance rows x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mahalanobis Distance — mahalanobis","text":"","code":"# S4 method for CompositionMatrix mahalanobis(x, center, cov, ..., robust = TRUE, method = c(\"mve\", \"mcd\"))  # S4 method for ILR mahalanobis(x, center, cov, ..., robust = TRUE, method = c(\"mve\", \"mcd\"))"},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mahalanobis Distance — mahalanobis","text":"x CompositionMatrix ILR object. center numeric vector giving mean vector distribution. missing, estimated x. cov numeric matrix giving covariance distribution. missing, estimated x. ... Extra parameters passed MASS::cov.rob(). used robust TRUE. robust logical scalar: robust location scatter estimation used? method character string specifying method used. must one \"mve\" (minimum volume ellipsoid) \"mcd\" (minimum covariance determinant). used robust TRUE.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mahalanobis Distance — mahalanobis","text":"numeric vector.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mahalanobis Distance — mahalanobis","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mahalanobis Distance — mahalanobis","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Mahalanobis distance mahalanobis(coda) #>  #>         H1         H2         H3         H4         H5         H6         H7  #>  5.1307933  0.5074407  2.1126269  2.8998211 19.0843749  5.6475980 33.6658536  #>         H8         H9        H10        H11        H12        H13        H14  #> 25.7137516  1.2502063 56.7461076  1.7542646  5.9925765  4.5827167 22.1196584  #>        H15        H16        H17        H18        H19        H20        H21  #>  1.9526275 25.7147694  7.6312860  2.1336759  2.5354194  4.4406867  2.3685685  #>        H22        H23        H24        H25  #>  3.7668943 25.9902036  6.7638903  6.5289073"},{"path":"https://packages.tesselle.org/nexus/reference/margin.html","id":null,"dir":"Reference","previous_headings":"","what":"Marginal Compositions — margin","title":"Marginal Compositions — margin","text":"Marginal Compositions","code":""},{"path":"https://packages.tesselle.org/nexus/reference/margin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Marginal Compositions — margin","text":"","code":"margin(x, ...)  # S4 method for CompositionMatrix margin(x, parts = c(1, 2), name = \"*\")"},{"path":"https://packages.tesselle.org/nexus/reference/margin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Marginal Compositions — margin","text":"x CompositionMatrix object. ... Currently used. parts integer character vector specifying columns selected. name character string giving name amalgamation column.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/margin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Marginal Compositions — margin","text":"CompositionMatrix object.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/margin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Marginal Compositions — margin","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/margin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Marginal Compositions — margin","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Marginal compositions mar <- margin(coda, parts = c(\"B\", \"D\")) head(mar) #> <CompositionMatrix: 6 x 3> #>            B         D         * #> H1 0.6328342 0.1277646 0.2394011 #> H2 0.4837434 0.1869933 0.3292633 #> H3 0.2163720 0.2258829 0.5577451 #> H4 0.4936486 0.2094895 0.2968618 #> H5 0.6888621 0.1384919 0.1726460 #> H6 0.5360281 0.2557386 0.2082334"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Compositional Mean — mean","title":"Compositional Mean — mean","text":"Compositional Mean","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compositional Mean — mean","text":"","code":"# S4 method for CompositionMatrix mean(x, ..., na.rm = FALSE)"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compositional Mean — mean","text":"x CompositionMatrix object. ... Currently used. na.rm logical scalar: missing values removed?","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compositional Mean — mean","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compositional Mean — mean","text":"Closed vector columns geometric means.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compositional Mean — mean","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compositional Mean — mean","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compositional Mean — mean","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Mean mean(coda) #>          A          B          C          D          E  #> 0.48941415 0.21966082 0.09881294 0.10405382 0.08805827   ## Metric variance metric_var(coda) #> [1] 1.691324  ## Metric standard deviation metric_sd(coda) #> [1] 0.6502546"},{"path":"https://packages.tesselle.org/nexus/reference/metric_var.html","id":null,"dir":"Reference","previous_headings":"","what":"Metric Variance and Standard Deviation — metric_var","title":"Metric Variance and Standard Deviation — metric_var","text":"metric_var() computes metric variance (total variance), .e. global measure spread. metric_sd() computes metric standard deviation.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/metric_var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metric Variance and Standard Deviation — metric_var","text":"","code":"metric_var(x, ...)  metric_sd(x, ...)  # S4 method for CompositionMatrix metric_var(x)  # S4 method for CompositionMatrix metric_sd(x)"},{"path":"https://packages.tesselle.org/nexus/reference/metric_var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metric Variance and Standard Deviation — metric_var","text":"x CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/metric_var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metric Variance and Standard Deviation — metric_var","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/metric_var.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Metric Variance and Standard Deviation — metric_var","text":"metric variance average CLR variances.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/metric_var.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Metric Variance and Standard Deviation — metric_var","text":"Boogaart, K. G. van den & Tolosana-Delgado, R. (2013). Analyzing Compositional Data R. Berlin Heidelberg: Springer-Verlag. doi:10.1007/978-3-642-36809-7 . Hron, K. & Kubáček. L. (2011). Statistical Properties Total Variation Estimator Compositional Data. Metrika, 74 (2): 221-230. doi:10.1007/s00184-010-0299-3 . Pawlowsky-Glahn, V. & Egozcue, J. J. (2001). Geometric Approach Statistical Analysis Simplex. Stochastic Environmental Research Risk Assessment, 15(5): 384-398. doi:10.1007/s004770100077 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/metric_var.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Metric Variance and Standard Deviation — metric_var","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/metric_var.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metric Variance and Standard Deviation — metric_var","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Mean mean(coda) #>          A          B          C          D          E  #> 0.48941415 0.21966082 0.09881294 0.10405382 0.08805827   ## Metric variance metric_var(coda) #> [1] 1.691324  ## Metric standard deviation metric_sd(coda) #> [1] 0.6502546"},{"path":"https://packages.tesselle.org/nexus/reference/mix.html","id":null,"dir":"Reference","previous_headings":"","what":"Mixed-Mode Analysis — mix","title":"Mixed-Mode Analysis — mix","text":"Mixes chemical petrographic matrices.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mixed-Mode Analysis — mix","text":"","code":"mix(x, y, ...)  # S4 method for matrix,matrix mix(x, y, lambda = 1, ...)  # S4 method for dist,dist mix(x, y, mu = 0.5)"},{"path":"https://packages.tesselle.org/nexus/reference/mix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mixed-Mode Analysis — mix","text":"x matrix chemical compositional data dissimilarity matrix chemical compositional data. y matrix coded mineralogical binary data dissimilarity matrix mineralogical data. ... Extra parameters passed cluster::daisy(). lambda length-one numeric vector giving weighting factor. mu length-one numeric vector lies 0 1 giving mixing parameter.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mixed-Mode Analysis — mix","text":"stats::dist object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mix.html","id":"methods-by-class-","dir":"Reference","previous_headings":"","what":"Methods (by class)","title":"Mixed-Mode Analysis — mix","text":"mix(x = matrix, y = matrix): First approach mixed-mode analysis. mix(x = dist, y = dist): Second approach mixed-mode analysis.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mix.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mixed-Mode Analysis — mix","text":"Experimental.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mix.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Mixed-Mode Analysis — mix","text":"Baxter, M. J., Beardah, C. C., Papageorgiou, ., Cau, M. ., Day, P. M. & Kilikoglou, V. (2008). Statistical Approaches Study Ceramic Artefacts Using Geochemical Petrographic Data. Archaeometry, 50(1): 142-157. doi:10.1111/j.1475-4754.2007.00359.x . Beardah, C. C., Baxter, M. J., Papageorgiou, . & Cau, M. . (2003). \"Mixed-Mode\" Approaches Grouping Ceramic Artefacts Using S-Plus. M. Doerr . Sarris, Digital Heritage Archaeology, p. 261-266. Athens: Archive Monuments Publications, Hellenic Ministry Culture. Gower, J. C. (1971). general coefficient similarity properties. Biometrics, 27(4):857-874. doi:10.2307/2528823 .","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mix.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mixed-Mode Analysis — mix","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/mix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mixed-Mode Analysis — mix","text":"","code":"# \\donttest{ ## Prepare chemical data data(\"chemistry\") major <- c(\"Fe2O3\", \"Al2O3\", \"MnO\", \"P2O5\", \"TiO2\", \"MgO\", \"CaO\", \"Na2O\", \"K2O\", \"SiO2\") chem <- chemistry[-1, major]  ## Prepare petrographic data data(\"petrography\") petro <- petrography[-c(7, 8), -1] petro <- cdt(petro) # Get the complete disjunctive table  ## First approach mix1 <- mix(as.matrix(chem), as.matrix(petro), lambda = 2) #> Warning: binary variable(s) 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 treated as interval scaled mds1 <- stats::cmdscale(mix1) # Multi-Dimensional Scaling plot(mds1)  # }"},{"path":"https://packages.tesselle.org/nexus/reference/nexus-package.html","id":null,"dir":"Reference","previous_headings":"","what":"nexus: Sourcing Archaeological Materials by Chemical Composition — nexus-package","title":"nexus: Sourcing Archaeological Materials by Chemical Composition — nexus-package","text":"Exploration analysis compositional data framework Aitchison (1986, ISBN: 978-94-010-8324-9). package provides tools chemical fingerprinting source tracking ancient materials.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/nexus-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package options","title":"nexus: Sourcing Archaeological Materials by Chemical Composition — nexus-package","text":"nexus uses following options() configure behavior: nexus.autodetect: logical scalar. Try automatically assign values corresponding slot CompositionMatrix object coercing data.frame? Defaults TRUE. nexus.verbose: logical scalar. R report extra information progress? Defaults TRUE.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/nexus-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"nexus: Sourcing Archaeological Materials by Chemical Composition — nexus-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Archéosciences Bordeaux (UMR 6034) Maison de l'Archéologie Université Bordeaux Montaigne F-33607 Pessac cedex France","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Outlier Detection — outliers","title":"Outlier Detection — outliers","text":"Outlier Detection","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Outlier Detection — outliers","text":"","code":"outliers(object, ...)  # S4 method for CompositionMatrix outliers(   object,   ...,   groups = get_groups(object),   robust = TRUE,   method = c(\"mve\", \"mcd\"),   quantile = 0.975 )"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Outlier Detection — outliers","text":"object CompositionMatrix. ... Extra parameters passed MASS::cov.rob(). used robust TRUE. groups factor sense .factor(groups) defines grouping. set, XXX. robust logical scalar: robust location scatter estimation used? method character string specifying method used. must one \"mve\" (minimum volume ellipsoid) \"mcd\" (minimum covariance determinant). used robust TRUE. quantile length-one numeric vector giving significance level. quantile used cut-value outlier detection: observations larger (squared) Mahalanobis distance considered potential outliers.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Outlier Detection — outliers","text":"OutlierIndex object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Outlier Detection — outliers","text":"outlier can defined large Mahalanobis distance observations. way, certain proportion observations can identified, e.g. top 2% values (.e. values 0.98th percentile Chi-2 distribution). one hand, Mahalanobis distance likely strongly affected presence outliers. Rousseeuw van Zomeren (1990) thus recommend using robust methods (excessively affected presence outliers). hand, choice threshold classifying observation outlier discussed. apparent reason particular threshold applicable data sets (Filzmoser, Garrett, Reimann 2005).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Outlier Detection — outliers","text":"Filzmoser, P., Garrett, R. G. & Reimann, C. (2005). Multivariate outlier detection exploration geochemistry. Computers & Geosciences, 31(5), 579-587. doi:10.1016/j.cageo.2004.11.013 . Filzmoser, P. & Hron, K. (2008). Outlier Detection Compositional Data Using Robust Methods. Mathematical Geosciences, 40(3), 233-248. doi:10.1007/s11004-007-9141-5 . Filzmoser, P., Hron, K. & Reimann, C. (2012). Interpretation multivariate outliers compositional data. Computers & Geosciences, 39, 77-85. doi:10.1016/j.cageo.2011.06.014 . Rousseeuw, P. J. & van Zomeren, B. C. (1990). Unmasking Multivariate Outliers Leverage Points. Journal American Statistical Association, 85(411): 633-639. doi:10.1080/01621459.1990.10474920 . Santos, F. (2020). Modern methods old data: overview robust methods outliers detection applications osteology. Journal Archaeological Science: Reports, 32, 102423. doi:10.1016/j.jasrep.2020.102423 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Outlier Detection — outliers","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Outlier Detection — outliers","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  ## Detect outliers out <- outliers(coda, groups = NULL, robust = FALSE)  plot(out) # Plot  plot(out, qq = TRUE) # Quantile-Quantile plot   ## Detect outliers by group out <- outliers(coda[, 1:15, drop = FALSE])  plot(out, ncol = 2) # Plot  plot(out, qq = TRUE, ncol = 4) # Quantile-Quantile plot"},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Components Analysis — pca_coda","title":"Principal Components Analysis — pca_coda","text":"Computes principal components analysis based singular value decomposition.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Components Analysis — pca_coda","text":"","code":"# S4 method for CompositionMatrix pca(   object,   center = TRUE,   scale = FALSE,   rank = NULL,   sup_row = NULL,   sup_col = NULL,   weight_row = NULL,   weight_col = NULL )  # S4 method for LogRatio pca(   object,   center = TRUE,   scale = FALSE,   rank = NULL,   sup_row = NULL,   sup_col = NULL,   weight_row = NULL,   weight_col = NULL )"},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Components Analysis — pca_coda","text":"object LogRatio object. center logical scalar: variables shifted zero centered? scale logical scalar: variables scaled unit variance? rank integer value specifying maximal number components kept results. NULL (default), \\(p - 1\\) components returned. sup_row vector specifying indices supplementary rows. sup_col vector specifying indices supplementary columns. weight_row numeric vector specifying active row (individual) weights. NULL (default), uniform weights used. Row weights internally normalized sum 1 weight_col numeric vector specifying active column (variable) weights. NULL (default), uniform weights (1) used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Components Analysis — pca_coda","text":"dimensio::PCA object. See package dimensio details.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Principal Components Analysis — pca_coda","text":"Aitchison, J. Greenacre, M. (2002). Biplots compositional data. Journal Royal Statistical Society: Series C (Applied Statistics), 51: 375-392. doi:10.1111/1467-9876.00275 . Filzmoser, P., Hron, K. Reimann, C. (2009). Principal component analysis compositional data outliers. Environmetrics, 20: 621-632. doi:10.1002/env.966 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Principal Components Analysis — pca_coda","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Components Analysis — pca_coda","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  ## Centered log-ratio clr <- transform_clr(coda)  ## PCA X <- pca(clr, scale = FALSE)  ## Explore results library(dimensio)  ## Plot viz_individuals(X, highlight = get_groups(coda), pch = 16)  viz_variables(X)"},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":null,"dir":"Reference","previous_headings":"","what":"Perturbation Operation — perturbation","title":"Perturbation Operation — perturbation","text":"Perturbation two compositions.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perturbation Operation — perturbation","text":"","code":"perturbation(x, y, ...)  # S4 method for numeric,numeric perturbation(x, y)  # S4 method for CompositionMatrix,numeric perturbation(x, y)  # S4 method for CompositionMatrix,matrix perturbation(x, y)"},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perturbation Operation — perturbation","text":"x, y numeric vector compositional data CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perturbation Operation — perturbation","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perturbation Operation — perturbation","text":"compositional geometry, perturbation plays role sum (translation). closed component-wise product two compositions.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Perturbation Operation — perturbation","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/perturbation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perturbation Operation — perturbation","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #>           V1        V2   V3 #> S1 0.4166667 0.8333333 0.75  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>     V1 V2  V3 #> S1 0.5  1 0.5  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/petrography.html","id":null,"dir":"Reference","previous_headings":"","what":"Can Sora Petrographic Data — petrography","title":"Can Sora Petrographic Data — petrography","text":"Can Sora Petrographic Data","code":""},{"path":"https://packages.tesselle.org/nexus/reference/petrography.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Can Sora Petrographic Data — petrography","text":"","code":"petrography"},{"path":"https://packages.tesselle.org/nexus/reference/petrography.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Can Sora Petrographic Data — petrography","text":"data.frame 21 variables: VAR1 Optical activity. VAR2 Inclusion orientation. VAR3 Void orientation. VAR4 Texture. VAR5 Special components. VAR6 Plutonic rocks. VAR7 Volcanic rocks. VAR8 Metamorphic rocks. VAR9 Sedimentary rocks. VAR10 Quartz. VAR11 Feldspar. VAR12 Plagioclase. VAR13 Pyroxenes. VAR14 Amphiboles. VAR15 Micas. VAR16 Phyllosilicates. VAR17 Carbonates. VAR18 constituents. VAR19 Packing.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/petrography.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Can Sora Petrographic Data — petrography","text":"Cau, M.-., Day, P. M., Baxter, M. J., Papageorgiou, ., Iliopoulos, . & Montana, G. (2004). Exploring Automatic Grouping Procedures Ceramic Petrology. Journal Archaeological Science, 31(9): 1325-1338. doi:10.1016/j.jas.2004.03.006 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Compositional Data — plot","title":"Plot Compositional Data — plot","text":"Displays matrix ternary plots.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Compositional Data — plot","text":"","code":"# S4 method for CompositionMatrix,missing plot(x, ..., margin = NULL)"},{"path":"https://packages.tesselle.org/nexus/reference/plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Compositional Data — plot","text":"x CompositionMatrix object. ... graphical parameters. margin character string integer giving index column used third part ternary plots. NULL (default), marginal compositions used (.e. geometric mean non-selected parts).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Compositional Data — plot","text":"plot() called side-effects: results graphic displayed (invisibly return x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Compositional Data — plot","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Compositional Data — plot","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Ternary plots plot(coda)"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Log-Ratios — plot_logratio","title":"Plot Log-Ratios — plot_logratio","text":"Displays density plot.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Log-Ratios — plot_logratio","text":"","code":"# S4 method for LogRatio,missing plot(   x,   ...,   order = NULL,   decreasing = FALSE,   groups = get_groups(x),   rug = TRUE,   ticksize = 0.05,   ncol = NULL,   flip = FALSE,   xlab = NULL,   ylab = NULL,   main = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   legend = list(x = \"topright\") )"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Log-Ratios — plot_logratio","text":"x LogRatio object. ... graphical parameters, particularly, border col. order logical scalar: ratio ordered? decreasing logical scalar: sort order increasing decreasing? groups factor sense .factor(groups) defines grouping. set, matrix panels defined groups drawn. rug logical scalar: rug representation (1-d plot) data added plot? ticksize length-one numeric vector giving length ticks making rug. Positive lengths give inwards ticks. used rug TRUE. ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) variable variable? xlab, ylab character vector giving x y axis labels. main character string giving main title plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? legend list additional arguments passed graphics::legend(); names list used argument names. NULL, legend displayed.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Log-Ratios — plot_logratio","text":"plot() called side-effects: results graphic displayed (invisibly return x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Log-Ratios — plot_logratio","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Log-Ratios — plot_logratio","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  ## Log ratio clr <- transform_clr(coda) plot(clr, group = NULL, flip = TRUE, border = \"black\", col = NA)  plot(clr, flip = TRUE)"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Outliers — plot_outliers","title":"Plot Outliers — plot_outliers","text":"Plot Outliers","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Outliers — plot_outliers","text":"","code":"# S4 method for OutlierIndex,missing plot(   x,   ...,   qq = FALSE,   probs = c(0.25, 0.75),   ncol = NULL,   flip = FALSE,   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL )"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Outliers — plot_outliers","text":"x OutlierIndex object. ... graphical parameters. qq logical scalar: quantile-quantile plot produced? probs length-two numeric vector representing probabilities. Corresponding quantile pairs define line drawn (see stats::qqline()). used qq TRUE. ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) group group? xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Outliers — plot_outliers","text":"plot() called side-effects: results graphic displayed (invisibly return x).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot Outliers — plot_outliers","text":"Filzmoser, P., Garrett, R. G. & Reimann, C. (2005). Multivariate outlier detection exploration geochemistry. Computers & Geosciences, 31(5), 579-587. doi:10.1016/j.cageo.2004.11.013 . Filzmoser, P. & Hron, K. (2008). Outlier Detection Compositional Data Using Robust Methods. Mathematical Geosciences, 40(3), 233-248. doi:10.1007/s11004-007-9141-5 . Filzmoser, P., Hron, K. & Reimann, C. (2012). Interpretation multivariate outliers compositional data. Computers & Geosciences, 39, 77-85. doi:10.1016/j.cageo.2011.06.014 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Outliers — plot_outliers","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Outliers — plot_outliers","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  ## Detect outliers out <- outliers(coda, groups = NULL, robust = FALSE)  plot(out) # Plot  plot(out, qq = TRUE) # Quantile-Quantile plot   ## Detect outliers by group out <- outliers(coda[, 1:15, drop = FALSE])  plot(out, ncol = 2) # Plot  plot(out, qq = TRUE, ncol = 4) # Quantile-Quantile plot"},{"path":"https://packages.tesselle.org/nexus/reference/policy.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing Values Policy — policy","title":"Missing Values Policy — policy","text":"Missing Values Policy","code":""},{"path":"https://packages.tesselle.org/nexus/reference/policy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Missing Values Policy — policy","text":"Compositional data quantitative (positive) descriptions parts whole, carrying relative, rather absolute, information (ie. relative changes relevant; Aitchison 1986). Basically, two situations can outlined: presence zeros: considered observed quantities, happen detection limit (thus interpreted small unknown values). presence missing values (NA): indicate quantities question observed. creating CompositionMatrix object, presence zero NA values allowed: makes possible explore visualize data preserving missing structure. However, user must deal missing values proceeding (e.g. removing incomplete cases replacing values concerned): log-ratio transformations computed presence missing values.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/policy.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Missing Values Policy — policy","text":"need advanced features (e.g. imputation missing values), consider compositions robCompositions package.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/policy.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Missing Values Policy — policy","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":null,"dir":"Reference","previous_headings":"","what":"Powering Operation — powering","title":"Powering Operation — powering","text":"Perturbation two compositions.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Powering Operation — powering","text":"","code":"powering(x, a, ...)  # S4 method for numeric,numeric powering(x, a)  # S4 method for CompositionMatrix,numeric powering(x, a)"},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Powering Operation — powering","text":"x numeric vector compositional data CompositionMatrix object. numeric constant. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Powering Operation — powering","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Powering Operation — powering","text":"compositional geometry, powering replaces product vector scalar (scaling) defined closed powering components given scalar.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Powering Operation — powering","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/powering.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Powering Operation — powering","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #>           V1        V2   V3 #> S1 0.4166667 0.8333333 0.75  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>     V1 V2  V3 #> S1 0.5  1 0.5  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. arkhe remove_NA, remove_zero, replace_NA, replace_zero dimensio cdt, pca","code":""},{"path":"https://packages.tesselle.org/nexus/reference/replace_NA.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing Values Replacement — missing","title":"Missing Values Replacement — missing","text":"Multiplicative replacement missing values.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/replace_NA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing Values Replacement — missing","text":"","code":"# S4 method for CompositionMatrix replace_NA(x, value)"},{"path":"https://packages.tesselle.org/nexus/reference/replace_NA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing Values Replacement — missing","text":"x CompositionMatrix object. value numeric vector giving replacement values.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/replace_NA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing Values Replacement — missing","text":"CompositionMatrix object, missing values replaced.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/replace_NA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Missing Values Replacement — missing","text":"Martín-Fernández, J. ., Barceló-Vidal, C. & Pawlowsky-Glahn, V. (2003). Dealing Zeros Missing Values Compositional Data Sets Using Nonparametric Imputation. Mathematical Geology, 35(3): 253-278. doi:10.1023/:1023866030544 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/replace_NA.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Missing Values Replacement — missing","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/replace_NA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing Values Replacement — missing","text":"","code":"## Data from Martín-Fernández et al. 2003 X <- data.frame(   X1 = c(0.0000, 0.1304, 0.1963),   X2 = c(0.1250, 0.3151, NA),   X3 = c(0.1237, NA, NA),   X4 = c(0.7253, 0.2002, 0.0819),   X5 = c(0.0260, 0.3543, 0.0114) )  ## Coerce to a compositional matrix Y <- as_composition(X)  ## Replace zeros Z <- replace_NA(Y, value = 0.2) Z #> <CompositionMatrix: 3 x 5> #>          X1      X2     X3        X4         X5 #> 1 0.0000000 0.12500 0.1237 0.7253000 0.02600000 #> 2 0.1043200 0.25208 0.2000 0.1601600 0.28344000 #> 3 0.4066989 0.20000 0.2000 0.1696823 0.02361878"},{"path":"https://packages.tesselle.org/nexus/reference/replace_zero.html","id":null,"dir":"Reference","previous_headings":"","what":"Zero-Replacement — zero","title":"Zero-Replacement — zero","text":"Multiplicative replacement zeros.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/replace_zero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zero-Replacement — zero","text":"","code":"# S4 method for CompositionMatrix replace_zero(x, value, delta = 2/3)"},{"path":"https://packages.tesselle.org/nexus/reference/replace_zero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zero-Replacement — zero","text":"x CompositionMatrix object. value numeric vector giving detection limits part (\\((0,1)\\)). delta numeric vector specifying fraction detection limit used replacement.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/replace_zero.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Zero-Replacement — zero","text":"CompositionMatrix object, zero values replaced.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/replace_zero.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Zero-Replacement — zero","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. Martín-Fernández, J. ., Barceló-Vidal, C. & Pawlowsky-Glahn, V. (2003). Dealing Zeros Missing Values Compositional Data Sets Using Nonparametric Imputation. Mathematical Geology, 35(3): 253-278. doi:10.1023/:1023866030544 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/replace_zero.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Zero-Replacement — zero","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/replace_zero.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Zero-Replacement — zero","text":"","code":"## Data from Martín-Fernández et al. 2003 X <- data.frame(   X1 = c(0.0000, 0.1304, 0.1963),   X2 = c(0.1250, 0.3151, NA),   X3 = c(0.1237, NA, NA),   X4 = c(0.7253, 0.2002, 0.0819),   X5 = c(0.0260, 0.3543, 0.0114) )  ## Coerce to a compositional matrix Y <- as_composition(X)  ## Replace zeros Z <- replace_zero(Y, value = 0.02, delta = 2/3) Z #> <CompositionMatrix: 3 x 5> #>           X1        X2        X3        X4         X5 #> 1 0.01333333 0.1233333 0.1220507 0.7156293 0.02565333 #> 2 0.13040000 0.3151000        NA 0.2002000 0.35430000 #> 3 0.67783149        NA        NA 0.2828039 0.03936464"},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Working With Samples — samples","title":"Working With Samples — samples","text":"Retrieves defines sample names.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Working With Samples — samples","text":"","code":"any_replicated(x)  is_replicated(x)  get_samples(x)  set_samples(x) <- value  # S4 method for CompositionMatrix is_replicated(x)  # S4 method for LogRatio is_replicated(x)  # S4 method for OutlierIndex is_replicated(x)  # S4 method for CompositionMatrix any_replicated(x)  # S4 method for LogRatio any_replicated(x)  # S4 method for OutlierIndex any_replicated(x)  # S4 method for CompositionMatrix get_samples(x)  # S4 method for LogRatio get_samples(x)  # S4 method for OutlierIndex get_samples(x)  # S4 method for CompositionMatrix set_samples(x) <- value"},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Working With Samples — samples","text":"x object get set samples. value possible value samples x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Working With Samples — samples","text":"set_samples() returns object sort x new sample names assigned. get_samples() returns character vector giving sample names x. any_replicated() returns logical scalar specifying whether x replicated observations. is_replicated() returns logical vector specifying whether observation replicate.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Working With Samples — samples","text":"situations, measurements may repeated (e.g. multiple chemical analyses sample). presence repeated measurements can specified giving several observations sample name. See vignette(\"nexus\").","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Working With Samples — samples","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":null,"dir":"Reference","previous_headings":"","what":"Scalar Product — scalar","title":"Scalar Product — scalar","text":"Computes Aitchison scalar product two compositions.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scalar Product — scalar","text":"","code":"scalar(x, y, ...)  # S4 method for numeric,numeric scalar(x, y)  # S4 method for CompositionMatrix,CompositionMatrix scalar(x, y)"},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scalar Product — scalar","text":"x, y CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scalar Product — scalar","text":"numeric vector.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Scalar Product — scalar","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scalar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scalar Product — scalar","text":"","code":"x <- as_composition(c(1, 2, 3)) y <- as_composition(c(1, 2, 1))  ## Perturbation perturbation(x, y) #> <CompositionMatrix: 1 x 3> #>       V1  V2    V3 #> S1 0.125 0.5 0.375 x + y #>           V1        V2   V3 #> S1 0.4166667 0.8333333 0.75  ## Powering powering(y, 2) #> <CompositionMatrix: 1 x 3> #>           V1        V2        V3 #> S1 0.1666667 0.6666667 0.1666667 y * 2 #> <CompositionMatrix: 1 x 3> #>     V1 V2  V3 #> S1 0.5  1 0.5  ## Scalar product scalar(x, y) #> [1] 0.06646867"},{"path":"https://packages.tesselle.org/nexus/reference/scale.html","id":null,"dir":"Reference","previous_headings":"","what":"Scaling and Centering of Compositional Data — scale","title":"Scaling and Centering of Compositional Data — scale","text":"Scaling Centering Compositional Data","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scaling and Centering of Compositional Data — scale","text":"","code":"# S4 method for CompositionMatrix scale(x, center = TRUE, scale = TRUE)"},{"path":"https://packages.tesselle.org/nexus/reference/scale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scaling and Centering of Compositional Data — scale","text":"x CompositionMatrix object. center logical scalar numeric vector giving center substracted. scale logical scalar length-one numeric vector giving scaling factor multiplication.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scaling and Centering of Compositional Data — scale","text":"CompositionMatrix object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scale.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Scaling and Centering of Compositional Data — scale","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. Boogaart, K. G. van den & Tolosana-Delgado, R. (2013). Analyzing Compositional Data R. Berlin Heidelberg: Springer-Verlag. doi:10.1007/978-3-642-36809-7 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/scale.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Scaling and Centering of Compositional Data — scale","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/scale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scaling and Centering of Compositional Data — scale","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Center and scale scaled <- scale(coda, center = TRUE, scale = TRUE) mean(scaled) #>   A   B   C   D   E  #> 0.2 0.2 0.2 0.2 0.2  head(scaled) #> <CompositionMatrix: 6 x 5> #>             A          B          C          D          E #> H1 0.21634296 0.40851878 0.04204451 0.09427149 0.23882226 #> H2 0.19541817 0.23027129 0.17084651 0.16234092 0.24112312 #> H3 0.05406075 0.01921842 0.73935593 0.07477711 0.11258779 #> H4 0.23277503 0.24975389 0.12625644 0.20672500 0.18448965 #> H5 0.17430188 0.54016862 0.02523291 0.12375232 0.13654426 #> H6 0.25315982 0.30580853 0.05187680 0.30958887 0.07956597"},{"path":"https://packages.tesselle.org/nexus/reference/slides.html","id":null,"dir":"Reference","previous_headings":"","what":"Thin Sections — slides","title":"Thin Sections — slides","text":"Mineral compositions five slides reported five analysts.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/slides.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Thin Sections — slides","text":"","code":"slides"},{"path":"https://packages.tesselle.org/nexus/reference/slides.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Thin Sections — slides","text":"data.frame 9 variables: analyst Analyst number. slide Slide number. quartz Quartz (percent). microcline Microcline (percent). plagioclass Plagioclass (percent). biotite Biotite (percent). plagioclass Plagioclass (percent). muscovite Muscovite (percent). opaques Opaque minerals (percent). nonopaques Non-opaque minerals (percent).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/slides.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Thin Sections — slides","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or Replace Parts of an Object — subset","title":"Extract or Replace Parts of an Object — subset","text":"Operators acting objects extract replace parts.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or Replace Parts of an Object — subset","text":"","code":"# S4 method for CompositionMatrix,missing,missing,missing [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,missing,missing,logical [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,index,missing,missing [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,index,missing,logical [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,missing,index,missing [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,missing,index,logical [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,index,index,missing [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix,index,index,logical [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix [(x, i, j, ...) <- value  # S4 method for CompositionMatrix [[(x, i, j, ...) <- value"},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or Replace Parts of an Object — subset","text":"x object extract element(s) replace element(s). , j Indices specifying elements extract replace. Indices numeric, integer character vectors empty (missing) NULL. Numeric values coerced integer .integer(). Character vectors matched name elements. empty index (comma separated blank) indicates entries dimension selected. ... Currently used. drop logical scalar: result coerced lowest possible dimension? works extracting elements, replacement. value possible value element(s) x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or Replace Parts of an Object — subset","text":"subsetted object sort x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"subcomposition","dir":"Reference","previous_headings":"","what":"Subcomposition","title":"Extract or Replace Parts of an Object — subset","text":"drop FALSE, subsetting possible components CompositionMatrix object produce closed subcomposition (see examples).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or Replace Parts of an Object — subset","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract or Replace Parts of an Object — subset","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite) head(coda) #> <CompositionMatrix: 6 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093 #> H2 0.482 0.238 0.090 0.092 0.098 #> H3 0.370 0.091 0.342 0.095 0.102 #> H4 0.509 0.238 0.072 0.101 0.080 #> H5 0.442 0.383 0.029 0.077 0.069 #> H6 0.523 0.262 0.042 0.125 0.048  ## Subset coda[[1, 1]] # Get the first value #> [1] 0.488 coda[1] # Get the first value #> [1] 0.488 coda[, ] # Get all values #> <CompositionMatrix: 25 x 5> #>             A         B          C          D          E #> H1  0.4880000 0.3170000 0.03800000 0.06400000 0.09300000 #> H2  0.4820000 0.2380000 0.09000000 0.09200000 0.09800000 #> H3  0.3700000 0.0910000 0.34200000 0.09500000 0.10200000 #> H4  0.5090000 0.2380000 0.07200000 0.10100000 0.08000000 #> H5  0.4420000 0.3830000 0.02900000 0.07700000 0.06900000 #> H6  0.5230000 0.2620000 0.04200000 0.12500000 0.04800000 #> H7  0.4460000 0.3300000 0.04600000 0.12200000 0.05600000 #> H8  0.3460000 0.0520000 0.42900000 0.09600000 0.07700000 #> H9  0.4120000 0.1170000 0.26700000 0.09600000 0.10800000 #> H10 0.4260000 0.4660000 0.00700000 0.05600000 0.04500000 #> H11 0.4990000 0.1950000 0.11400000 0.09500000 0.09700000 #> H12 0.4520000 0.3730000 0.02700000 0.05500000 0.09300000 #> H13 0.3270000 0.0850000 0.38900000 0.08000000 0.11900000 #> H14 0.4140000 0.1290000 0.23400000 0.15800000 0.06500000 #> H15 0.4620000 0.1750000 0.15800000 0.08300000 0.12200000 #> H16 0.3230000 0.0730000 0.40900000 0.12900000 0.06600000 #> H17 0.4320000 0.4430000 0.01000000 0.07800000 0.03700000 #> H18 0.4954955 0.3233233 0.03103103 0.08708709 0.06306306 #> H19 0.4230000 0.1580000 0.20400000 0.08300000 0.13200000 #> H20 0.4460000 0.1150000 0.23800000 0.11600000 0.08500000 #> H21 0.4580000 0.1660000 0.16800000 0.12000000 0.08800000 #> H22 0.4990000 0.2500000 0.06800000 0.10900000 0.07400000 #> H23 0.4860000 0.3400000 0.02500000 0.09400000 0.05500000 #> H24 0.4550000 0.1660000 0.17600000 0.09600000 0.10700000 #> H25 0.4590000 0.2490000 0.09700000 0.09800000 0.09700000 coda[1, , drop = FALSE] # Get the first row #> <CompositionMatrix: 1 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093  ## Subcomposition subcoda <- coda[, 1:3, drop = FALSE] # Get the first three column head(subcoda) #> <CompositionMatrix: 6 x 3> #>        A     B     C #> H1 0.488 0.317 0.038 #> H2 0.482 0.238 0.090 #> H3 0.370 0.091 0.342 #> H4 0.509 0.238 0.072 #> H5 0.442 0.383 0.029 #> H6 0.523 0.262 0.042"},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":null,"dir":"Reference","previous_headings":"","what":"Row Sums — totals","title":"Row Sums — totals","text":"Retrieves defines row sums (closure).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Row Sums — totals","text":"","code":"get_totals(x)  set_totals(x) <- value  # S4 method for CompositionMatrix get_totals(x)  # S4 method for CompositionMatrix set_totals(x) <- value"},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Row Sums — totals","text":"x object get set totals. value possible value totals x.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Row Sums — totals","text":"set_totals() returns object sort x new row sums assigned. get_totals() returns row sums x.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Row Sums — totals","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Row Sums — totals","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies get_totals(B) #>  [1] 297 353 234 406 214 199 222  93 237 203 313 304 216 303 266 255 237 116 175 #> [20] 350  ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to a data.frame X <- data.frame(B) head(X) #>           V1         V2         V3        V4         V5 #> S1 0.3097643 0.31313131 0.01683502 0.1515152 0.20875421 #> S2 0.2662890 0.21246459 0.27762040 0.1444759 0.09915014 #> S3 0.3418803 0.05555556 0.05128205 0.3162393 0.23504274 #> S4 0.2216749 0.22906404 0.11330049 0.2413793 0.19458128 #> S5 0.2897196 0.10280374 0.26635514 0.2570093 0.08411215 #> S6 0.2462312 0.07537688 0.48241206 0.1859296 0.01005025"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":null,"dir":"Reference","previous_headings":"","what":"Additive Log-Ratios (ALR) — transform_alr","title":"Additive Log-Ratios (ALR) — transform_alr","text":"Computes ALR transformation.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Additive Log-Ratios (ALR) — transform_alr","text":"","code":"transform_alr(object, ...)  # S4 method for CompositionMatrix transform_alr(object, j = ncol(object))"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Additive Log-Ratios (ALR) — transform_alr","text":"object CompositionMatrix object. ... Currently used. j integer giving index rationing part (denominator).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Additive Log-Ratios (ALR) — transform_alr","text":"ALR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Additive Log-Ratios (ALR) — transform_alr","text":"ALR transformation logratio pair parts respect fixed part.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Additive Log-Ratios (ALR) — transform_alr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press. Greenacre, M. J. (2021). Compositional Data Analysis. Annual Review Statistics Application, 8(1): 271-299. doi:10.1146/annurev-statistics-042720-124436 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Additive Log-Ratios (ALR) — transform_alr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Additive Log-Ratios (ALR) — transform_alr","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":null,"dir":"Reference","previous_headings":"","what":"Centered Log-Ratios (CLR) — transform_clr","title":"Centered Log-Ratios (CLR) — transform_clr","text":"Computes CLR transformation.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Centered Log-Ratios (CLR) — transform_clr","text":"","code":"transform_clr(object, ...)  # S4 method for CompositionMatrix transform_clr(object, weights = FALSE)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Centered Log-Ratios (CLR) — transform_clr","text":"object CompositionMatrix object. ... Currently used. weights logical scalar: sould varying weight used. FALSE (default), equally-weighted parts used. Alternatively, positive numeric vector weights can specified.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Centered Log-Ratios (CLR) — transform_clr","text":"CLR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Centered Log-Ratios (CLR) — transform_clr","text":"CLR transformation computes log part relative geometric mean parts.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Centered Log-Ratios (CLR) — transform_clr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press. Greenacre, M. J. (2021). Compositional Data Analysis. Annual Review Statistics Application, 8(1): 271-299. doi:10.1146/annurev-statistics-042720-124436 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Centered Log-Ratios (CLR) — transform_clr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Centered Log-Ratios (CLR) — transform_clr","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":null,"dir":"Reference","previous_headings":"","what":"Isometric Log-Ratios (ILR) — transform_ilr","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"Computes ILR transformations.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"","code":"transform_ilr(object, ...)  # S4 method for CompositionMatrix transform_ilr(object)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"object CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"ILR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"ILR transformation provides coordinates composition respect given orthonormal basis. transform_ilr() uses orthonormal basis (Helmert matrix) originally defined Egozcue et al. (2003).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barceló-Vidal, C. (2003). Isometric Logratio Transformations Compositional Data Analysis. Mathematical Geology, 35(3), 279-300. doi:10.1023/:1023818214614 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press. Greenacre, M. J. (2021). Compositional Data Analysis. Annual Review Statistics Application, 8(1): 271-299. doi:10.1146/annurev-statistics-042720-124436 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isometric Log-Ratios (ILR) — transform_ilr","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse Log-Ratio Transformation — transform_inverse","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"Computes inverse log-ratio transformations.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"","code":"transform_inverse(object, origin, ...)  # S4 method for CLR,missing transform_inverse(object)  # S4 method for ALR,missing transform_inverse(object)  # S4 method for ILR,missing transform_inverse(object)  # S4 method for matrix,ILR transform_inverse(object, origin)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"object LogRatio object. origin LogRatio object used inverse transformation. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"CompositionMatrix object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barceló-Vidal, C. (2003). Isometric Logratio Transformations Compositional Data Analysis. Mathematical Geology, 35(3), 279-300. doi:10.1023/:1023818214614 . Fišerová, E. & Hron, K. (2011). Interpretation Orthonormal Coordinates Compositional Data. Mathematical Geosciences, 43(4), 455‑468. doi:10.1007/s11004-011-9333-x . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse Log-Ratio Transformation — transform_inverse","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairwise Log-Ratios (LR) — transform_lr","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"Computes pairwise log-ratio transformation.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"","code":"transform_lr(object, ...)  # S4 method for CompositionMatrix transform_lr(object)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"object CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"LR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press. Greenacre, M. J. (2021). Compositional Data Analysis. Annual Review Statistics Application, 8(1): 271-299. doi:10.1146/annurev-statistics-042720-124436 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairwise Log-Ratios (LR) — transform_lr","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":null,"dir":"Reference","previous_headings":"","what":"Pivot Log-Ratios (PLR) — transform_plr","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"Computes PLR transformations.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"","code":"transform_plr(object, ...)  # S4 method for CompositionMatrix transform_plr(object, pivot = 1)"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"object CompositionMatrix object. ... Currently used. pivot integer giving index pivotal variable.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"PLR object.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"Fišerová, E. & Hron, K. (2011). Interpretation Orthonormal Coordinates Compositional Data. Mathematical Geosciences, 43(4), 455‑468. doi:10.1007/s11004-011-9333-x . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press. Greenacre, M. J. (2021). Compositional Data Analysis. Annual Review Statistics Application, 8(1): 271-299. doi:10.1146/annurev-statistics-042720-124436 . Hron, K., Filzmoser, P., de Caritat, P., Fišerová, E. & Gardlo, . (2017). Weighted Pivot Coordinates Compositional Data Application Geochemical Mapping. Mathematical Geosciences, 49(6), 797-814. doi:10.1007/s11004-017-9684-z .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pivot Log-Ratios (PLR) — transform_plr","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":null,"dir":"Reference","previous_headings":"","what":"Variation Matrix — variation","title":"Variation Matrix — variation","text":"Computes variation matrix (Aitchison 1986, definition 4.4).","code":""},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variation Matrix — variation","text":"","code":"variation(x, ...)  # S4 method for CompositionMatrix variation(x)"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variation Matrix — variation","text":"x CompositionMatrix object. ... Currently used.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variation Matrix — variation","text":"matrix.","code":""},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Variation Matrix — variation","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Variation Matrix — variation","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variation Matrix — variation","text":"","code":"## Data from Aitchison 1986 data(\"hongite\")  ## Coerce to compositional data coda <- as_composition(hongite)  ## Variation matrix ## (Aitchison 1986, definition 4.4) (varia <- variation(coda)) #>            A         B         C          D         E #> A 0.00000000 0.2592742 1.5328586 0.08281464 0.1385604 #> B 0.25927416 0.0000000 3.0006804 0.54727146 0.6490135 #> C 1.53285862 3.0006804 0.0000000 1.11145146 0.9476375 #> D 0.08281464 0.5472715 1.1114515 0.00000000 0.1870579 #> E 0.13856035 0.6490135 0.9476375 0.18705791 0.0000000  ## Cluster dendrogram d <- as.dist(varia) h <- hclust(d, method = \"ward.D2\") plot(h)"}]