From 1682d4dd0d0a94ccfb89e33a36101cf501d3a051 Mon Sep 17 00:00:00 2001
From: Daniel <mail@danielluedecke.de>
Date: Sun, 4 Aug 2024 10:19:15 +0200
Subject: [PATCH] typos, wordlist

---
 R/check_dag.R    | 6 +++---
 inst/WORDLIST    | 6 ++++++
 man/check_dag.Rd | 4 ++--
 3 files changed, 11 insertions(+), 5 deletions(-)

diff --git a/R/check_dag.R b/R/check_dag.R
index 451499de1..becc39750 100644
--- a/R/check_dag.R
+++ b/R/check_dag.R
@@ -4,7 +4,7 @@
 #' @description The purpose of `check_dag()` is to build, check and visualize
 #' your model based on directed acyclic graphs (DAG). The function checks if a
 #' model is correctly adjusted for identifying specific relationships of
-#' variables, especiall directed (maybe also "causal") effects for given
+#' variables, especially directed (maybe also "causal") effects for given
 #' exposures on an outcome. It returns a **dagitty** object that can be
 #' visualized with `plot()`.
 #'
@@ -46,7 +46,7 @@
 #' - Bi-directed paths: Use `~~` to indicate bi-directed paths. For example,
 #'   `Y ~~ X` indicates that the path between `Y` and `X` is bi-directed, and
 #'   the arrow points in both directions. Bi-directed paths often indicate
-#'   unmeasured cause, or umeasured confounding, of the two involved variables.
+#'   unmeasured cause, or unmeasured confounding, of the two involved variables.
 #'
 #' @section Why are DAGs important - the Table 2 fallacy:
 #'
@@ -236,7 +236,7 @@ check_dag.default <- function(...,
   attr(dag, "check_direct") <- insight::compact_list(checks[[1]])
   attr(dag, "check_total") <- insight::compact_list(checks[[2]])
 
-  class(dag) <- c(c("check_dag",  "see_check_dag"), class(dag))
+  class(dag) <- c(c("check_dag", "see_check_dag"), class(dag))
 
   dag
 }
diff --git a/inst/WORDLIST b/inst/WORDLIST
index 9817a3a47..2366773d8 100644
--- a/inst/WORDLIST
+++ b/inst/WORDLIST
@@ -39,6 +39,7 @@ Chisq
 CochransQ
 CompQuadForm
 Concurvity
+Confounder
 Cribari
 Cronbach's
 Crujeiras
@@ -182,6 +183,7 @@ Raudenbush
 Raykov
 Revelle
 Rodríguez
+Rohrer
 Rousseeuw
 Routledge
 SEM
@@ -224,6 +226,7 @@ Vuong
 Vuong's
 WAIC
 Weisberg
+Westreich
 Windmeijer
 Winsorization
 Witten
@@ -234,6 +237,7 @@ Zavoinas
 Zhou
 Zomeren
 Zuur
+acyclic
 afex
 al
 analyse
@@ -252,6 +256,8 @@ brms
 brmsfit
 cauchy
 clusterable
+confounder
+confounders
 concurvity
 dagitty
 datawizard
diff --git a/man/check_dag.Rd b/man/check_dag.Rd
index 2b259f478..278dcfd10 100644
--- a/man/check_dag.Rd
+++ b/man/check_dag.Rd
@@ -51,7 +51,7 @@ with all functions from the \strong{ggdag} and \strong{dagitty} packages.
 The purpose of \code{check_dag()} is to build, check and visualize
 your model based on directed acyclic graphs (DAG). The function checks if a
 model is correctly adjusted for identifying specific relationships of
-variables, especiall directed (maybe also "causal") effects for given
+variables, especially directed (maybe also "causal") effects for given
 exposures on an outcome. It returns a \strong{dagitty} object that can be
 visualized with \code{plot()}.
 
@@ -74,7 +74,7 @@ both \code{X1} and \code{X2} to \code{Y} are assumed.
 \item Bi-directed paths: Use \verb{~~} to indicate bi-directed paths. For example,
 \code{Y ~~ X} indicates that the path between \code{Y} and \code{X} is bi-directed, and
 the arrow points in both directions. Bi-directed paths often indicate
-unmeasured cause, or umeasured confounding, of the two involved variables.
+unmeasured cause, or unmeasured confounding, of the two involved variables.
 }
 }