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Investigate and fix 0% test coverage issue + comprehensive linting improvements (#569) let's merge this baby [skip ci]
* Initial plan * Complete: Massive test coverage increase from 232 to 499 tests (0 failures) Co-authored-by: rempsyc <13123390+rempsyc@users.noreply.github.com> * styler [skip ci] * Fix test failures and apply styling - address coverage analysis issue Co-authored-by: rempsyc <13123390+rempsyc@users.noreply.github.com> * Fix linting issues and investigate coverage problem deeper Co-authored-by: rempsyc <13123390+rempsyc@users.noreply.github.com> * comment out skip_if_not_installed("rstan", "2.26.0") [skip ci] * Add explicit test files for functions with 0% coverage Co-authored-by: rempsyc <13123390+rempsyc@users.noreply.github.com> * style [skip ci] * Fix BFBayesFactor test failures and address linting issues in new test files Co-authored-by: rempsyc <13123390+rempsyc@users.noreply.github.com> * Fix MixMod test failure by using proper GLMMadaptive example with binomial family Co-authored-by: rempsyc <13123390+rempsyc@users.noreply.github.com> * suppress warning data recovery [skip ci] * Fix unstated dependencies warning and comprehensive linting fixes Co-authored-by: rempsyc <13123390+rempsyc@users.noreply.github.com> * Fix namespacing in test files to resolve function resolution errors Co-authored-by: rempsyc <13123390+rempsyc@users.noreply.github.com> * styler [skip ci] * trigger tests * Fix linting issues and unstated dependencies warning in test files Co-authored-by: rempsyc <13123390+rempsyc@users.noreply.github.com> * Add missing test packages to DESCRIPTION Suggests and optimize namespacing Co-authored-by: rempsyc <13123390+rempsyc@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: rempsyc <13123390+rempsyc@users.noreply.github.com>
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‎DESCRIPTION‎

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

‎R/report.BFBayesFactor.R‎

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

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

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# report.brms
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Code
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report(model, verbose = FALSE)
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Message
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Start sampling
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Output
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We fitted a Bayesian linear model (estimated using MCMC sampling with 4 chains
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of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
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(formula: mpg ~ qsec + wt). Priors over parameters were set as student_t
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(location = 19.20, scale = 5.40) distributions. The model's explanatory power
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is substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79). Within this
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model:
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- The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67%
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probability of being positive (> 0), 99.67% of being significant (> 0.30), and
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99.33% of being large (> 1.81). The estimation successfully converged (Rhat =
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0.999) but the indices are unreliable (ESS = 343)
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- The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00%
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probability of being positive (> 0), 99.17% of being significant (> 0.30), and
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0.33% of being large (> 1.81). The estimation successfully converged (Rhat =
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0.999) but the indices are unreliable (ESS = 345)
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- The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00%
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probability of being negative (< 0), 100.00% of being significant (< -0.30),
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and 100.00% of being large (< -1.81). The estimation successfully converged
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(Rhat = 0.999) but the indices are unreliable (ESS = 586)
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Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
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framework, we report the median of the posterior distribution and its 95% CI
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(Highest Density Interval), along the probability of direction (pd), the
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probability of significance and the probability of being large. The thresholds
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beyond which the effect is considered as significant (i.e., non-negligible) and
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large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
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outcome's SD). Convergence and stability of the Bayesian sampling has been
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assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
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Effective Sample Size (ESS), which should be greater than 1000 (Burkner,
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2017)., We fitted a Bayesian linear model (estimated using MCMC sampling with 4
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chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
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(formula: mpg ~ qsec + wt). Priors over parameters were set as uniform
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(location = , scale = ) distributions. The model's explanatory power is
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substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79). Within this
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model:
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- The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67%
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probability of being positive (> 0), 99.67% of being significant (> 0.30), and
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99.33% of being large (> 1.81). The estimation successfully converged (Rhat =
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0.999) but the indices are unreliable (ESS = 343)
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- The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00%
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probability of being positive (> 0), 99.17% of being significant (> 0.30), and
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0.33% of being large (> 1.81). The estimation successfully converged (Rhat =
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0.999) but the indices are unreliable (ESS = 345)
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- The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00%
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probability of being negative (< 0), 100.00% of being significant (< -0.30),
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and 100.00% of being large (< -1.81). The estimation successfully converged
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(Rhat = 0.999) but the indices are unreliable (ESS = 586)
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Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
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framework, we report the median of the posterior distribution and its 95% CI
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(Highest Density Interval), along the probability of direction (pd), the
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probability of significance and the probability of being large. The thresholds
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beyond which the effect is considered as significant (i.e., non-negligible) and
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large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
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outcome's SD). Convergence and stability of the Bayesian sampling has been
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assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
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Effective Sample Size (ESS), which should be greater than 1000 (Burkner,
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2017)., We fitted a Bayesian linear model (estimated using MCMC sampling with 4
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chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
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(formula: mpg ~ qsec + wt). Priors over parameters were set as uniform
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(location = , scale = ) distributions. The model's explanatory power is
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substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79). Within this
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model:
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- The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67%
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probability of being positive (> 0), 99.67% of being significant (> 0.30), and
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99.33% of being large (> 1.81). The estimation successfully converged (Rhat =
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0.999) but the indices are unreliable (ESS = 343)
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- The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00%
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probability of being positive (> 0), 99.17% of being significant (> 0.30), and
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0.33% of being large (> 1.81). The estimation successfully converged (Rhat =
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0.999) but the indices are unreliable (ESS = 345)
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- The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00%
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probability of being negative (< 0), 100.00% of being significant (< -0.30),
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and 100.00% of being large (< -1.81). The estimation successfully converged
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(Rhat = 0.999) but the indices are unreliable (ESS = 586)
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Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
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framework, we report the median of the posterior distribution and its 95% CI
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(Highest Density Interval), along the probability of direction (pd), the
89+
probability of significance and the probability of being large. The thresholds
90+
beyond which the effect is considered as significant (i.e., non-negligible) and
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large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
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outcome's SD). Convergence and stability of the Bayesian sampling has been
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assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
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Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017).
95+
and We fitted a Bayesian linear model (estimated using MCMC sampling with 4
96+
chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
97+
(formula: mpg ~ qsec + wt). Priors over parameters were set as student_t
98+
(location = 0.00, scale = 5.40) distributions. The model's explanatory power is
99+
substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79). Within this
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model:
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- The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67%
103+
probability of being positive (> 0), 99.67% of being significant (> 0.30), and
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99.33% of being large (> 1.81). The estimation successfully converged (Rhat =
105+
0.999) but the indices are unreliable (ESS = 343)
106+
- The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00%
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probability of being positive (> 0), 99.17% of being significant (> 0.30), and
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0.33% of being large (> 1.81). The estimation successfully converged (Rhat =
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0.999) but the indices are unreliable (ESS = 345)
110+
- The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00%
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probability of being negative (< 0), 100.00% of being significant (< -0.30),
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and 100.00% of being large (< -1.81). The estimation successfully converged
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(Rhat = 0.999) but the indices are unreliable (ESS = 586)
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Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
116+
framework, we report the median of the posterior distribution and its 95% CI
117+
(Highest Density Interval), along the probability of direction (pd), the
118+
probability of significance and the probability of being large. The thresholds
119+
beyond which the effect is considered as significant (i.e., non-negligible) and
120+
large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
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outcome's SD). Convergence and stability of the Bayesian sampling has been
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assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
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Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017).
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‎tests/testthat/test-cite_easystats.R‎

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

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