better skips to avoid long check times

tests/testthat/test-ecpe.R

@@ -1,18 +1,22 @@
-if (!identical(Sys.getenv("NOT_CRAN"), "true")) skip("No MCMC on CRAN")
-
-out <- capture.output(
-  suppressMessages(
-    cmds_ecpe_lcdm <- measr_dcm(
-      data = ecpe_data, missing = NA, qmatrix = ecpe_qmatrix,
-      resp_id = "resp_id", item_id = "item_id", type = "lcdm",
-      method = "optim", seed = 63277, backend = "cmdstanr",
-      prior = c(prior(uniform(-15, 15), class = "intercept"),
-                prior(uniform(0, 15), class = "maineffect"),
-                prior(uniform(-15, 15), class = "interaction")))
+if (!identical(Sys.getenv("NOT_CRAN"), "true")) {
+  skip("No MCMC on CRAN")
+} else {
+  out <- capture.output(
+    suppressMessages(
+      cmds_ecpe_lcdm <- measr_dcm(
+        data = ecpe_data, missing = NA, qmatrix = ecpe_qmatrix,
+        resp_id = "resp_id", item_id = "item_id", type = "lcdm",
+        method = "optim", seed = 63277, backend = "cmdstanr",
+        prior = c(prior(uniform(-15, 15), class = "intercept"),
+                  prior(uniform(0, 15), class = "maineffect"),
+                  prior(uniform(-15, 15), class = "interaction")))
+    )
   )
-)
+}
 
 test_that("lcdm model works for ecpe", {
+  skip_on_cran()
+
   expect_s3_class(cmds_ecpe_lcdm, "measrfit")
   expect_s3_class(cmds_ecpe_lcdm, "measrdcm")
   expect_equal(names(cmds_ecpe_lcdm),
@@ -64,6 +68,8 @@ test_that("lcdm model works for ecpe", {
 })
 
 test_that("extract ecpe", {
+  skip_on_cran()
+
   lcdm_param <- measr_extract(cmds_ecpe_lcdm, "item_param")
   all_param <- get_parameters(ecpe_qmatrix, item_id = "item_id",
                               type = "lcdm") %>%
@@ -105,6 +111,8 @@ test_that("extract ecpe", {
 })
 
 test_that("ecpe probabilities are accurate", {
+  skip_on_cran()
+
   ecpe_preds <- predict(cmds_ecpe_lcdm, newdata = ecpe_data,
                         resp_id = "resp_id")
 
@@ -176,6 +184,8 @@ test_that("ecpe probabilities are accurate", {
 })
 
 test_that("ecpe reliability", {
+  skip_on_cran()
+
   ecpe_reli <- reliability(cmds_ecpe_lcdm)
 
   # list naming
@@ -247,6 +257,8 @@ test_that("ecpe reliability", {
 })
 
 test_that("m2 calculation is correct", {
+  skip_on_cran()
+
   m2 <- fit_m2(cmds_ecpe_lcdm)
 
   expect_equal(m2$m2, 507.0756, tolerance = 0.1)
@@ -262,6 +274,8 @@ test_that("m2 calculation is correct", {
 })
 
 test_that("mcmc requirements error", {
+  skip_on_cran()
+
   err <- rlang::catch_cnd(add_fit(cmds_ecpe_lcdm, method = "ppmc"))
   expect_s3_class(err, "error_bad_method")
   expect_match(err$message, "`method = \"mcmc\"`")

tests/testthat/test-mcmc.R

@@ -1,33 +1,37 @@
-if (!identical(Sys.getenv("NOT_CRAN"), "true")) skip("No MCMC on CRAN")
-
-out <- capture.output(
-  suppressMessages(
-    cmds_mdm_lcdm <- measr_dcm(
-      data = mdm_data, missing = NA, qmatrix = mdm_qmatrix,
-      resp_id = "respondent", item_id = "item", type = "lcdm",
-      method = "mcmc", seed = 63277, backend = "cmdstanr",
-      iter_sampling = 500, iter_warmup = 1000, chains = 2,
-      parallel_chains = 2,
-      prior = c(prior(uniform(-15, 15), class = "intercept"),
-                prior(uniform(0, 15), class = "maineffect")))
+if (!identical(Sys.getenv("NOT_CRAN"), "true")) {
+  skip("No MCMC on CRAN")
+} else {
+  out <- capture.output(
+    suppressMessages(
+      cmds_mdm_lcdm <- measr_dcm(
+        data = mdm_data, missing = NA, qmatrix = mdm_qmatrix,
+        resp_id = "respondent", item_id = "item", type = "lcdm",
+        method = "mcmc", seed = 63277, backend = "cmdstanr",
+        iter_sampling = 500, iter_warmup = 1000, chains = 2,
+        parallel_chains = 2,
+        prior = c(prior(uniform(-15, 15), class = "intercept"),
+                  prior(uniform(0, 15), class = "maineffect")))
+    )
   )
-)
-
-out <- capture.output(
-  suppressMessages(
-    cmds_mdm_dina <- measr_dcm(
-      data = mdm_data, missing = NA, qmatrix = mdm_qmatrix,
-      resp_id = "respondent", item_id = "item", type = "dina",
-      attribute_structure = "independent",
-      method = "mcmc", seed = 63277, backend = "rstan",
-      iter = 1500, warmup = 1000, chains = 2,
-      cores = 2,
-      prior = c(prior(beta(5, 17), class = "slip"),
-                prior(beta(5, 17), class = "guess")))
+
+  out <- capture.output(
+    suppressMessages(
+      cmds_mdm_dina <- measr_dcm(
+        data = mdm_data, missing = NA, qmatrix = mdm_qmatrix,
+        resp_id = "respondent", item_id = "item", type = "dina",
+        attribute_structure = "independent",
+        method = "mcmc", seed = 63277, backend = "rstan",
+        iter = 1500, warmup = 1000, chains = 2,
+        cores = 2,
+        prior = c(prior(beta(5, 17), class = "slip"),
+                  prior(beta(5, 17), class = "guess")))
+    )
   )
-)
+}
 
 test_that("as_draws works", {
+  skip_on_cran()
+
   draws <- as_draws(cmds_mdm_dina)
   expect_s3_class(draws, "draws_array")
 
@@ -48,6 +52,8 @@ test_that("as_draws works", {
 })
 
 test_that("get_mcmc_draws works as expected", {
+  skip_on_cran()
+
   test_draws <- get_mcmc_draws(cmds_mdm_lcdm)
   expect_equal(posterior::ndraws(test_draws), 1000)
   expect_equal(posterior::nvariables(test_draws), 10)
@@ -60,13 +66,17 @@ test_that("get_mcmc_draws works as expected", {
 })
 
 test_that("log_lik is calculated correctly", {
+  skip_on_cran()
+
   log_lik <- prep_loglik_array(cmds_mdm_lcdm)
 
   # expected value from 2-class LCA fit in Mplus
   expect_equal(sum(apply(log_lik, c(3), mean)), -331.764, tolerance = 1.000)
 })
 
 test_that("loo and waic work", {
+  skip_on_cran()
+
   err <- rlang::catch_cnd(loo(rstn_dina))
   expect_s3_class(err, "error_bad_method")
   expect_match(err$message, "`method = \"mcmc\"`")
@@ -83,6 +93,8 @@ test_that("loo and waic work", {
 })
 
 test_that("loo and waic can be added to model", {
+  skip_on_cran()
+
   err <- rlang::catch_cnd(measr_extract(cmds_mdm_lcdm, "loo"))
   expect_s3_class(err, "rlang_error")
   expect_match(err$message, "LOO criterion must be added")
@@ -114,6 +126,8 @@ test_that("loo and waic can be added to model", {
 })
 
 test_that("model comparisons work", {
+  skip_on_cran()
+
   err <- rlang::catch_cnd(loo_compare(cmds_mdm_lcdm, cmds_mdm_dina))
   expect_s3_class(err, "error_missing_criterion")
   expect_match(err$message, "does not contain a precomputed")
@@ -147,6 +161,8 @@ test_that("model comparisons work", {
 })
 
 test_that("ppmc works", {
+  skip_on_cran()
+
   test_ppmc <- fit_ppmc(cmds_mdm_lcdm, model_fit = character(),
                         item_fit = character())
   expect_equal(test_ppmc, list())
@@ -214,6 +230,8 @@ test_that("ppmc works", {
 })
 
 test_that("ppmc extraction errors", {
+  skip_on_cran()
+
   err <- rlang::catch_cnd(measr_extract(cmds_mdm_lcdm, "ppmc_raw_score"))
   expect_s3_class(err, "rlang_error")
   expect_match(err$message, "Model fit information must be added")
@@ -237,6 +255,8 @@ test_that("ppmc extraction errors", {
 })
 
 test_that("model fit can be added", {
+  skip_on_cran()
+
   test_model <- cmds_mdm_dina
   expect_equal(test_model$fit, list())
 
@@ -321,6 +341,8 @@ test_that("model fit can be added", {
 })
 
 test_that("respondent probabilities are correct", {
+  skip_on_cran()
+
   mdm_preds <- predict(cmds_mdm_lcdm, newdata = mdm_data,
                        resp_id = "respondent", summary = TRUE)
   mdm_full_preds <- predict(cmds_mdm_lcdm, summary = FALSE)