Revert c1d4e8 - run tests calling Stan on CRAN

tests/testthat/test-nma.R

@@ -167,6 +167,40 @@ test_that("nma() error if missing values in outcomes or predictors", {
         regression = ~x1, center = TRUE)), m)
 })
 
+smknet_3l <- combine_network(
+  set_agd_arm(smkdummy %>% mutate(extra = 0.5, tclass = if_else(trtn == 1, 0, 1)),
+              studyn, trtn, r = r, n = n, trt_class = tclass),
+  set_ipd(ipddummy %>% mutate(extra = TRUE, tclass = if_else(trtn == 1, 0, 1)),
+          studyn, trtn, r = r, trt_class = tclass),
+  trt_ref = 1) %>%
+  add_integration(x1 = distr(qnorm, x1_mean, x1_sd),
+                  x2 = distr(qbinom, 1, x2),
+                  x3 = distr(qnorm, x3_mean, x3_sd))
+
+smknet_3c <- combine_network(
+  set_agd_arm(smkdummy %>% mutate(extra = 0.5, tclass = if_else(trtn == 1, 0, 1)),
+              studyn, trtn, r = r, n = n, trt_class = tclass),
+  set_ipd(ipddummy %>% mutate(extra = "a", tclass = if_else(trtn == 1, 0, 1)),
+          studyn, trtn, r = r, trt_class = tclass),
+  trt_ref = 1) %>%
+  add_integration(x1 = distr(qnorm, x1_mean, x1_sd),
+                  x2 = distr(qbinom, 1, x2),
+                  x3 = distr(qnorm, x3_mean, x3_sd))
+
+test_that("nma() silently drops unnecessary columns", {
+  expect_error(nma(smknet_3l, regression = ~(x1 + x2 + x3):.trt,
+                    prior_intercept = normal(0, 10),
+                    prior_trt = normal(0, 10),
+                    prior_reg = normal(0, 5),
+                    test_grad = TRUE), NA)
+
+  expect_error(nma(smknet_3c, regression = ~(x1 + x2 + x3):.trt,
+                    prior_intercept = normal(0, 10),
+                    prior_trt = normal(0, 10),
+                    prior_reg = normal(0, 5),
+                    test_grad = TRUE), NA)
+})
+
 test_that("nma.fit() error if only one of x or y provided", {
   x <- matrix(1, nrow = 3, ncol = 3)
   colnames(x) <- c("a", "b", "c")
@@ -252,45 +286,6 @@ test_that("nma.fit() error if agd_contrast_Sigma is not right dimensions", {
 })
 
 
-# avoid running Stan models on CRAN
-skip_on_cran()
-
-
-smknet_3l <- combine_network(
-  set_agd_arm(smkdummy %>% mutate(extra = 0.5, tclass = if_else(trtn == 1, 0, 1)),
-              studyn, trtn, r = r, n = n, trt_class = tclass),
-  set_ipd(ipddummy %>% mutate(extra = TRUE, tclass = if_else(trtn == 1, 0, 1)),
-          studyn, trtn, r = r, trt_class = tclass),
-  trt_ref = 1) %>%
-  add_integration(x1 = distr(qnorm, x1_mean, x1_sd),
-                  x2 = distr(qbinom, 1, x2),
-                  x3 = distr(qnorm, x3_mean, x3_sd))
-
-smknet_3c <- combine_network(
-  set_agd_arm(smkdummy %>% mutate(extra = 0.5, tclass = if_else(trtn == 1, 0, 1)),
-              studyn, trtn, r = r, n = n, trt_class = tclass),
-  set_ipd(ipddummy %>% mutate(extra = "a", tclass = if_else(trtn == 1, 0, 1)),
-          studyn, trtn, r = r, trt_class = tclass),
-  trt_ref = 1) %>%
-  add_integration(x1 = distr(qnorm, x1_mean, x1_sd),
-                  x2 = distr(qbinom, 1, x2),
-                  x3 = distr(qnorm, x3_mean, x3_sd))
-
-test_that("nma() silently drops unnecessary columns", {
-  expect_error(nma(smknet_3l, regression = ~(x1 + x2 + x3):.trt,
-                   prior_intercept = normal(0, 10),
-                   prior_trt = normal(0, 10),
-                   prior_reg = normal(0, 5),
-                   test_grad = TRUE), NA)
-
-  expect_error(nma(smknet_3c, regression = ~(x1 + x2 + x3):.trt,
-                   prior_intercept = normal(0, 10),
-                   prior_trt = normal(0, 10),
-                   prior_reg = normal(0, 5),
-                   test_grad = TRUE), NA)
-})
-
-
 single_study_a <- tibble(study = "A", trt = c("a", "b"), r = 500, n = 1000)
 net_ss_a <- set_agd_arm(single_study_a, study, trt, r = r, n = n)
 

tests/testthat/test-nodesplit.R

@@ -179,6 +179,18 @@ test_that("nma() nodesplit error if no comparisons to split", {
   )
 })
 
+test_that("nma() nodesplit warning about ignoring comparisons", {
+  expect_warning(
+    nma(thrombo_net,
+        consistency = "nodesplit",
+        nodesplit = data.frame(trt1 = "Acc t-PA", trt2 = c("TNK", "ASPAC")),
+        prior_intercept = normal(scale = 10),
+        prior_trt = normal(scale = 10),
+        test_grad = TRUE),
+    "Ignoring node-split comparisons.+Acc t-PA vs\\. TNK"
+  )
+})
+
 test_that("nma() nodesplit argument validation", {
   m1 <- "The data frame passed to `nodesplit` should have two columns"
 
@@ -221,22 +233,6 @@ test_that("nma() nodesplit argument validation", {
   )
 })
 
-
-# avoid running Stan models on CRAN
-skip_on_cran()
-
-test_that("nma() nodesplit warning about ignoring comparisons", {
-  expect_warning(
-    nma(thrombo_net,
-        consistency = "nodesplit",
-        nodesplit = data.frame(trt1 = "Acc t-PA", trt2 = c("TNK", "ASPAC")),
-        prior_intercept = normal(scale = 10),
-        prior_trt = normal(scale = 10),
-        test_grad = TRUE),
-    "Ignoring node-split comparisons.+Acc t-PA vs\\. TNK"
-  )
-})
-
 thrombo_ns1 <- nma(thrombo_net,
                    consistency = "nodesplit",
                    nodesplit = c("SK", "t-PA"),

tests/testthat/test-plot_integration_error.R

@@ -1,7 +1,4 @@
 
-# avoid running Stan models on CRAN
-skip_on_cran()
-
 smk_net <- set_agd_arm(smoking,
                        study = studyn,
                        trt = trtc,

tests/testthat/test-posterior_ranks.R

@@ -1,7 +1,4 @@
 
-# avoid running Stan models on CRAN
-skip_on_cran()
-
 smk_net <- set_agd_arm(smoking,
                        study = studyn,
                        trt = trtc,

tests/testthat/test-predict.R

@@ -1,7 +1,4 @@
 
-# avoid running Stan models on CRAN
-skip_on_cran()
-
 smk_net <- set_agd_arm(smoking,
                        study = studyn,
                        trt = trtc,

tests/testthat/test-relative_effects.R

@@ -1,7 +1,4 @@
 
-# avoid running Stan models on CRAN
-skip_on_cran()
-
 smk_net <- set_agd_arm(smoking,
                        study = studyn,
                        trt = trtc,