Dev

DESCRIPTION

@@ -23,10 +23,12 @@ Imports:
     admtools (>= 0.3.0)
 Suggests: 
     knitr,
-    rmarkdown
+    rmarkdown,
+    testthat (>= 3.0.0)
 VignetteBuilder: knitr
 Depends: 
     R (>= 4.2)
 LazyData: true
 URL: https://mindthegap-erc.github.io/StratPal/ , https://github.com/MindTheGap-ERC/StratPal
 BugReports: https://github.com/MindTheGap-ERC/StratPal/issues
+Config/testthat/edition: 3

R/snd_niche.R

@@ -34,6 +34,13 @@ snd_niche = function(opt, tol, prob_modifier = 1, cutoff_val = NULL){
     #' The collection probability follows the shape of a bell curve across a gradient, where `opt` determines the peak (mean) of the bell curve, and `tol` the standard deviation. "snd" stands for "scaled normal distribution", as the collection probability has the shape of the probability density of the normal distribution.
   #' @references * Holland, Steven M. and Patzkowsky, Mark E. 1999. "Models for simulating the fossil record." Geology. https://doi.org/10.1130/0091-7613(1999)027%3C0491:MFSTFR%3E2.3.CO;2
 
+  if (tol <= 0){
+    stop("Need positive \"tol\" parameter.")
+  }
+  if (prob_modifier <= 0){
+    stop("Need positive \"prob_modifier\" parameter.")
+  }
+
   fa = function(x){
     f_inv  = stats::dnorm(opt, opt, tol)
     y = pmin(prob_modifier/f_inv * stats::dnorm(x, opt, tol), rep(1, length(x)))

tests/testthat.R

@@ -0,0 +1,12 @@
+# This file is part of the standard setup for testthat.
+# It is recommended that you do not modify it.
+#
+# Where should you do additional test configuration?
+# Learn more about the roles of various files in:
+# * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
+# * https://testthat.r-lib.org/articles/special-files.html
+
+library(testthat)
+library(StratPal)
+
+test_check("StratPal")

tests/testthat/test_bounded_niche.R

@@ -0,0 +1,12 @@
+test_that("disordered boundaries throw error", {
+  expect_error(bounded_niche(1,0))
+})
+
+test_that("correct values are returned",{
+  x1 = 0
+  x2 = 1
+  f = bounded_niche(x1, x2)
+  expect_equal(f(x1 - 1), 0)
+  expect_equal(f(x2 + 1), 0)
+  expect_equal(f(mean(c(x1, x2))), 1)
+})

tests/testthat/test_ornstein_uhlenbeck.R

@@ -0,0 +1,6 @@
+test_that("initial value is correct", {
+  y0 = 1
+  t = seq(0, 1, by = 0.05)
+  r = ornstein_uhlenbeck(t, y0 = y0)
+  expect_equal(r$y[1],y0)
+})

tests/testthat/test_p3.R

@@ -0,0 +1,12 @@
+test_that("throws error for negative rates", {
+  expect_error(p3(0, 0, 1))
+})
+
+test_that("throws error for flipped boundaries", {
+  expect_error(p3(1, 1, 0))
+})
+
+test_that("contitioning on number of samples works",{
+  n = 10
+  expect_equal(length(p3(1, 0, 1, n = n)), n )
+})

tests/testthat/test_p3_var_rate.R

@@ -0,0 +1,12 @@
+test_that("throws error for flipped boundary",{
+  expect_error(p3_var_rate(x = function(x) 1, from =  1, to = 0))
+})
+
+test_that("throws warning for too low values of f_max",{
+  expect_warning(p3_var_rate(x = function(x) 1000, from = 0,to = 1, n = 1000))
+})
+
+test_that("retruns correct number of events under conditioning",{
+  n = 10
+  expect_equal(length(p3_var_rate(function(x) 1, from = 0, to = 1, n = n)), n)
+})

tests/testthat/test_random_walk.R

@@ -0,0 +1,17 @@
+test_that("deterministic case is correct",{
+  t = seq(0, 1, by = 0.05)
+  r = random_walk(t, sigma = 0)
+  expect_equal(r$y, rep(0, length(t)))
+  mu = 2
+  r = random_walk(t, sigma = 0, mu = mu)
+  expect_equal(r$y, mu * t)
+})
+
+test_that("initial value is correct",{
+  y0 = 2
+  t = seq(0, 1, by = 0.05)
+  r = random_walk(t, y0 = y0)
+  expect_equal(r$y[1], y0)
+  r = random_walk(t - 2, y0 = y0)
+  expect_equal(r$y[1], y0)
+})

tests/testthat/test_rej_samp.R

@@ -0,0 +1,12 @@
+test_that("boundary flips throw error",{
+  expect_error(rej_samp(function (x) 1, 1,0))
+})
+
+test_that("retruns correct no of samples",{
+  n = 10
+  expect_equal(length(rej_samp(function(x) 1, 0, 1)), 1)
+})
+
+test_that("throws warning if density is too high",{
+  expect_warning(rej_samp(function(x) 10^6, 0, 1, n = 1000))
+})

tests/testthat/test_snd_niche.R

@@ -0,0 +1,27 @@
+test_that("values at highest points are correct",{
+  opt = 0
+  prob_mod = 1
+  f = snd_niche(opt,tol = 1, prob_modifier = prob_mod)
+  expect_equal(f(opt), prob_mod)
+  opt = 0
+  prob_mod = 0.5
+  f = snd_niche(opt,tol = 1, prob_modifier = prob_mod)
+  expect_equal(f(opt), prob_mod)
+  opt = 0
+  prob_mod = 2
+  f = snd_niche(opt,tol = 1, prob_modifier = prob_mod)
+  expect_equal(f(opt), 1)
+})
+
+test_that("cut off works", {
+  opt = 1
+  tol = 1
+  cut_off = 0
+  f = snd_niche(opt, tol, cutoff_val = cut_off )
+  expect_equal(f(cut_off), 0)
+})
+
+test_that("errors for negative tolerances and prob modifiers are thrown", {
+  expect_error(snd_niche(1,0))
+  expect_error(snd_niche(1,1,0))
+})

tests/testthat/test_thin.R

@@ -0,0 +1,10 @@
+test_that("thinning reduces number of events",{
+  x = p3(1, 0, 1, n = 100)
+  th = thin(x, function(x) 0.5)
+  expect_true(length(th) < length(x))
+})
+
+test_that("thinning with 1 keeps data identical",{
+  x = p3(1, 0, 1, n = 100)
+  expect_equal(thin(x, function(x) 1), x)
+})