apply styler

Author: MalteKurz

tests/testthat/test-double_ml_pliv_exception_handling.R

@@ -65,14 +65,15 @@ test_that("Unit tests of exception handling for DoubleMLPLIV", {
   ml_m = lrn("regr.ranger")
   ml_r = lrn("regr.ranger")
   ml_g = lrn("regr.ranger")
-  
-  
-  msg = paste0("A learner ml_g has been provided for ",
-               "score = 'partialling out' but will be ignored.")
+
+
+  msg = paste0(
+    "A learner ml_g has been provided for ",
+    "score = 'partialling out' but will be ignored.")
   expect_warning(DoubleMLPLIV$new(dml_data_pliv,
-                                  ml_l = ml_l, ml_m = ml_m, ml_r = ml_r,
-                                  ml_g = ml_g,
-                                  score = "partialling out"),
-                 regexp = msg)
+    ml_l = ml_l, ml_m = ml_m, ml_r = ml_r,
+    ml_g = ml_g,
+    score = "partialling out"),
+  regexp = msg)
 }
 )

tests/testthat/test-double_ml_pliv_parameter_passing.R

@@ -129,7 +129,7 @@ patrick::with_parameters_test_that("Unit tests for parameter passing of PLIV (no
     train_ids = list(my_sampling$train_set(1))
     test_ids = list(my_sampling$test_set(1))
     smpls = list(list(train_ids = train_ids, test_ids = test_ids))
-    
+
     if (score == "IV-type") {
       ml_g = mlr3::lrn(learner_pars$mlmethod$mlmethod_g)
     } else {

tests/testthat/test-double_ml_pliv_tuning.R

@@ -62,7 +62,7 @@ patrick::with_parameters_test_that("Unit tests for tuning of PLIV",
     data_ml = double_ml_data_from_data_frame(df,
       y_col = "y",
       d_cols = "d", x_cols = Xnames, z_cols = z_cols)
-    
+
     if (score == "IV-type") {
       ml_g = learner
     } else {

tests/testthat/test-double_ml_plr_exception_handling.R

@@ -124,20 +124,21 @@ patrick::with_parameters_test_that("Unit tests for exception handling of PLR:",
       msg = "Multiplier bootstrap has not yet been performed. First call bootstrap\\(\\) and then try confint\\(\\) again."
       expect_error(double_mlplr_obj$confint(joint = TRUE, level = 0.95),
         regexp = msg)
-      
+
       set.seed(3141)
       dml_data = make_plr_CCDDHNR2018(n_obs = 101)
       ml_l = lrn("regr.ranger")
       ml_m = ml_l$clone()
       ml_g = ml_l$clone()
-      
+
       if (score == "partialling out") {
-        msg = paste0("A learner ml_g has been provided for ",
-                     "score = 'partialling out' but will be ignored.")
+        msg = paste0(
+          "A learner ml_g has been provided for ",
+          "score = 'partialling out' but will be ignored.")
         expect_warning(DoubleMLPLR$new(dml_data,
-                                       ml_l = ml_l, ml_m = ml_m, ml_g=ml_g,
-                                       score = score),
-                       regexp = msg)
+          ml_l = ml_l, ml_m = ml_m, ml_g = ml_g,
+          score = score),
+        regexp = msg)
       }
     }
   }

tests/testthat/test-double_ml_plr_export_preds.R

@@ -31,7 +31,7 @@ patrick::with_parameters_test_that("Unit tests for for the export of predictions
     set.seed(3141)
     df = data_plr$df
     dml_data = data_plr$dml_data
-    
+
     if (score == "IV-type") {
       ml_g = lrn(g_learner)
     } else {

tests/testthat/test-double_ml_plr_multitreat.R

@@ -61,7 +61,7 @@ patrick::with_parameters_test_that("Unit tests for PLR:",
     data_ml = double_ml_data_from_data_frame(data_plr_multi,
       y_col = "y",
       d_cols = c("d1", "d2", "d3"), x_cols = Xnames)
-    
+
     if (score == "IV-type") {
       ml_g = learner_pars$ml_g$clone()
     } else {

tests/testthat/test-double_ml_plr_parameter_passing.R

@@ -141,7 +141,7 @@ patrick::with_parameters_test_that("Unit tests for parameter passing of PLR (no
     train_ids = list(my_sampling$train_set(1))
     test_ids = list(my_sampling$test_set(1))
     smpls = list(list(train_ids = train_ids, test_ids = test_ids))
-    
+
     if (score == "IV-type") {
       ml_g = mlr3::lrn(learner_pars$mlmethod$mlmethod_g)
     } else {

tests/testthat/test-double_ml_plr_set_samples.R

@@ -34,7 +34,7 @@ patrick::with_parameters_test_that("PLR with external sample provision:",
     data_ml = double_ml_data_from_data_frame(data_plr$df,
       y_col = "y",
       d_cols = "d", x_cols = Xnames)
-    
+
     if (score == "IV-type") {
       ml_g = learner_pars$ml_g$clone()
     } else {