Investigate and fix 0% test coverage issue + comprehensive linting improvements

DESCRIPTION

@@ -68,10 +68,14 @@ Suggests:
     BayesFactor,
     brms,
     collapse,
+    GLMMadaptive,
+    glmmTMB,
     ivreg,
     knitr,
     lavaan,
     lme4,
+    loo,
+    nlme,
     dplyr,
     Formula,
     rmarkdown,

R/report.BFBayesFactor.R

@@ -37,7 +37,6 @@ report.BFBayesFactor <- function(x, h0 = "H0", h1 = "H1", ...) {
   bf <- param$BF
   other_dir <- ifelse(bf < 1, "h0", "h1")
 
-
   if (other_dir == "h1") {
     other_text <- paste0(
       "There is ",
@@ -46,7 +45,9 @@ report.BFBayesFactor <- function(x, h0 = "H0", h1 = "H1", ...) {
       h1,
       " over ",
       h0,
-      " (", report_statistics(x, ...), ")."
+      " (",
+      report_statistics(x, ...),
+      ")."
     )
   } else {
     other_text <- paste0(
@@ -56,7 +57,9 @@ report.BFBayesFactor <- function(x, h0 = "H0", h1 = "H1", ...) {
       h0,
       " over ",
       h1,
-      " (", report_statistics(x, ...), ")."
+      " (",
+      report_statistics(x, ...),
+      ")."
     )
   }
   other_text
@@ -78,7 +81,8 @@ report_statistics.BFBayesFactor <- function(x, table = NULL, ...) {
   }
 
   bf <- table$BF
-  other_text <- ifelse(bf < 1,
+  other_text <- ifelse(
+    bf < 1,
     insight::format_bf(1 / bf, name = "BF01", ...),
     insight::format_bf(bf, name = "BF10", ...)
   )

tests/testthat/_snaps/windows/report.brmsfit.md

@@ -0,0 +1,124 @@
+# report.brms
+
+    Code
+      report(model, verbose = FALSE)
+    Message
+      Start sampling
+    Output
+      We fitted a Bayesian linear model (estimated using MCMC sampling with 4 chains
+      of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
+      (formula: mpg ~ qsec + wt). Priors over parameters were set as student_t
+      (location = 19.20, scale = 5.40) distributions. The model's explanatory power
+      is substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
+      model:
+
+        - The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67%
+      probability of being positive (> 0), 99.67% of being significant (> 0.30), and
+      99.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 343)
+        - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00%
+      probability of being positive (> 0), 99.17% of being significant (> 0.30), and
+      0.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 345)
+        - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00%
+      probability of being negative (< 0), 100.00% of being significant (< -0.30),
+      and 100.00% of being large (< -1.81). The estimation successfully converged
+      (Rhat = 0.999) but the indices are unreliable (ESS = 586)
+
+      Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
+      framework, we report the median of the posterior distribution and its 95% CI
+      (Highest Density Interval), along the probability of direction (pd), the
+      probability of significance and the probability of being large. The thresholds
+      beyond which the effect is considered as significant (i.e., non-negligible) and
+      large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
+      outcome's SD). Convergence and stability of the Bayesian sampling has been
+      assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
+      Effective Sample Size (ESS), which should be greater than 1000 (Burkner,
+      2017)., We fitted a Bayesian linear model (estimated using MCMC sampling with 4
+      chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
+      (formula: mpg ~ qsec + wt). Priors over parameters were set as uniform
+      (location = , scale = ) distributions. The model's explanatory power is
+      substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
+      model:
+
+        - The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67%
+      probability of being positive (> 0), 99.67% of being significant (> 0.30), and
+      99.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 343)
+        - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00%
+      probability of being positive (> 0), 99.17% of being significant (> 0.30), and
+      0.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 345)
+        - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00%
+      probability of being negative (< 0), 100.00% of being significant (< -0.30),
+      and 100.00% of being large (< -1.81). The estimation successfully converged
+      (Rhat = 0.999) but the indices are unreliable (ESS = 586)
+
+      Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
+      framework, we report the median of the posterior distribution and its 95% CI
+      (Highest Density Interval), along the probability of direction (pd), the
+      probability of significance and the probability of being large. The thresholds
+      beyond which the effect is considered as significant (i.e., non-negligible) and
+      large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
+      outcome's SD). Convergence and stability of the Bayesian sampling has been
+      assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
+      Effective Sample Size (ESS), which should be greater than 1000 (Burkner,
+      2017)., We fitted a Bayesian linear model (estimated using MCMC sampling with 4
+      chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
+      (formula: mpg ~ qsec + wt). Priors over parameters were set as uniform
+      (location = , scale = ) distributions. The model's explanatory power is
+      substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
+      model:
+
+        - The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67%
+      probability of being positive (> 0), 99.67% of being significant (> 0.30), and
+      99.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 343)
+        - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00%
+      probability of being positive (> 0), 99.17% of being significant (> 0.30), and
+      0.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 345)
+        - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00%
+      probability of being negative (< 0), 100.00% of being significant (< -0.30),
+      and 100.00% of being large (< -1.81). The estimation successfully converged
+      (Rhat = 0.999) but the indices are unreliable (ESS = 586)
+
+      Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
+      framework, we report the median of the posterior distribution and its 95% CI
+      (Highest Density Interval), along the probability of direction (pd), the
+      probability of significance and the probability of being large. The thresholds
+      beyond which the effect is considered as significant (i.e., non-negligible) and
+      large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
+      outcome's SD). Convergence and stability of the Bayesian sampling has been
+      assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
+      Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017).
+      and We fitted a Bayesian linear model (estimated using MCMC sampling with 4
+      chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
+      (formula: mpg ~ qsec + wt). Priors over parameters were set as student_t
+      (location = 0.00, scale = 5.40) distributions. The model's explanatory power is
+      substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
+      model:
+
+        - The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67%
+      probability of being positive (> 0), 99.67% of being significant (> 0.30), and
+      99.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 343)
+        - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00%
+      probability of being positive (> 0), 99.17% of being significant (> 0.30), and
+      0.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 345)
+        - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00%
+      probability of being negative (< 0), 100.00% of being significant (< -0.30),
+      and 100.00% of being large (< -1.81). The estimation successfully converged
+      (Rhat = 0.999) but the indices are unreliable (ESS = 586)
+
+      Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
+      framework, we report the median of the posterior distribution and its 95% CI
+      (Highest Density Interval), along the probability of direction (pd), the
+      probability of significance and the probability of being large. The thresholds
+      beyond which the effect is considered as significant (i.e., non-negligible) and
+      large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
+      outcome's SD). Convergence and stability of the Bayesian sampling has been
+      assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
+      Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017).
+

tests/testthat/test-cite_easystats.R

@@ -0,0 +1,120 @@
+test_that("cite_easystats() works with default parameters", {
+  result <- cite_easystats()
+  expect_s3_class(result, "cite_easystats")
+  expect_type(result, "list")
+  expect_named(result, c("intext", "refs"))
+  expect_type(result$intext, "character")
+  expect_type(result$refs, "character")
+  expect_match(result$intext, "easystats")
+  expect_match(result$refs, "Lüdecke")
+})
+
+test_that("cite_easystats() works with different formats", {
+  # Text format (default)
+  result_text <- cite_easystats(format = "text")
+  expect_match(result_text$intext, "collection of packages")
+  expect_match(result_text$refs, "- Lüdecke")
+
+  # Markdown format
+  result_md <- cite_easystats(format = "markdown")
+  expect_match(result_md$intext, "@easystatsPackage")
+  expect_match(result_md$refs, "id: easystatsPackage")
+
+  # BibLaTeX format
+  result_bib <- cite_easystats(format = "biblatex")
+  expect_match(result_bib$intext, "\\\\cite\\{")
+  expect_match(result_bib$refs, "@software\\{")
+})
+
+test_that("cite_easystats() works with different package specifications", {
+  # Single package
+  result_single <- cite_easystats(packages = "insight")
+  expect_true(any(grepl("insight", result_single$refs, fixed = TRUE)))
+
+  # Multiple specific packages
+  result_multi <- cite_easystats(packages = c("insight", "parameters"))
+  expect_true(any(grepl("insight", result_multi$refs, fixed = TRUE)))
+  expect_true(any(grepl("parameters", result_multi$refs, fixed = TRUE)))
+})
+
+test_that("cite_easystats() handles prefix and suffix correctly", {
+  # With default prefix/suffix
+  result_default <- cite_easystats()
+  expect_match(result_default$intext, "^Analyses were conducted")
+  expect_match(result_default$intext, "\\.$")
+
+  # Without prefix
+  result_no_prefix <- cite_easystats(intext_prefix = FALSE)
+  expect_false(startsWith(result_no_prefix$intext, "Analyses were conducted"))
+
+  # Without suffix
+  result_no_suffix <- cite_easystats(intext_suffix = FALSE)
+  expect_false(endsWith(result_no_suffix$intext, "."))
+
+  # Custom prefix and suffix
+  result_custom <- cite_easystats(
+    intext_prefix = "Custom prefix ",
+    intext_suffix = " custom suffix"
+  )
+  expect_match(result_custom$intext, "^Custom prefix")
+  expect_match(result_custom$intext, "custom suffix$")
+})
+
+test_that("cite_easystats() handles missing packages gracefully", {
+  # Test with non-existent package (should show warning and omit)
+  expect_message(
+    cite_easystats(packages = c("insight", "nonexistent_package")),
+    "not installed"
+  )
+
+  # Now get the result for testing
+  suppressMessages({
+    result <- cite_easystats(packages = c("insight", "nonexistent_package"))
+  })
+  expect_true(any(grepl("insight", result$refs, fixed = TRUE)))
+  expect_false(any(grepl("nonexistent", result$refs, fixed = TRUE)))
+})
+
+test_that("print.cite_easystats() works correctly", {
+  result <- cite_easystats()
+
+  # Test different 'what' arguments
+  expect_output(print(result, what = "all"), "Thanks for crediting")
+  expect_output(print(result, what = "intext"), "easystats")
+  expect_output(print(result, what = "refs"), "Lüdecke")
+
+  # Test with 'cite' and 'bib' aliases
+  expect_output(print(result, what = "cite"), "easystats")
+  expect_output(print(result, what = "bib"), "Lüdecke")
+})
+
+test_that("summary.cite_easystats() works correctly", {
+  result <- cite_easystats()
+
+  # Test different 'what' arguments
+  expect_output(summary(result, what = "all"), "Citations")
+  expect_output(summary(result, what = "intext"), "easystats")
+  expect_output(summary(result, what = "refs"), "Lüdecke")
+
+  # Test with 'cite' and 'bib' aliases
+  expect_output(summary(result, what = "cite"), "easystats")
+  expect_output(summary(result, what = "bib"), "Lüdecke")
+})
+
+test_that(".disamguation_letters() helper function works", {
+  # Test with logical vector
+  result1 <- report:::.disamguation_letters(c(TRUE, FALSE, TRUE))
+  expect_identical(result1, c("a", "", "b"))
+
+  result2 <- report:::.disamguation_letters(TRUE)
+  expect_identical(result2, "a") # Single TRUE should return "a", not empty
+
+  result3 <- report:::.disamguation_letters(c(FALSE, FALSE))
+  expect_identical(result3, c("", ""))
+
+  # Test error handling
+  expect_error(
+    report:::.disamguation_letters(c(1, 2, 3)),
+    "must be a logical vector"
+  )
+})