Updated indentation

Author: andrewherren

R/bart.R

@@ -250,16 +250,16 @@ bart <- function(
   drop_vars_variance <- variance_forest_params_updated$drop_vars
   num_features_subsample_variance <- variance_forest_params_updated$num_features_subsample
 
-    # Set a function-scoped RNG if user provided a random seed
-    custom_rng <- random_seed >= 0
-    if (custom_rng) {
-        # Store original global environment RNG state
-        original_global_seed <- .Random.seed
-        # Set new seed and store associated RNG state
-        set.seed(random_seed)
-        function_scoped_seed <- .Random.seed
-    }
-    
+  # Set a function-scoped RNG if user provided a random seed
+  custom_rng <- random_seed >= 0
+  if (custom_rng) {
+    # Store original global environment RNG state
+    original_global_seed <- .Random.seed
+    # Set new seed and store associated RNG state
+    set.seed(random_seed)
+    function_scoped_seed <- .Random.seed
+  }
+
   # Check if there are enough GFR samples to seed num_chains samplers
   if (num_gfr > 0) {
     if (num_chains > num_gfr) {
@@ -1748,27 +1748,27 @@ bart <- function(
   }
   class(result) <- "bartmodel"
 
-    # Clean up classes with external pointers to C++ data structures
-    if (include_mean_forest) {
-        rm(forest_model_mean)
-    }
-    if (include_variance_forest) {
-        rm(forest_model_variance)
-    }
-    rm(forest_dataset_train)
-    if (has_test) {
-        rm(forest_dataset_test)
-    }
-    if (has_rfx) {
-        rm(rfx_dataset_train, rfx_tracker_train, rfx_model)
-    }
-    rm(outcome_train)
-    rm(rng)
-    
-    # Restore global RNG state if user provided a random seed
-    if (custom_rng) {
-        .Random.seed <- original_global_seed
-    }
+  # Clean up classes with external pointers to C++ data structures
+  if (include_mean_forest) {
+    rm(forest_model_mean)
+  }
+  if (include_variance_forest) {
+    rm(forest_model_variance)
+  }
+  rm(forest_dataset_train)
+  if (has_test) {
+    rm(forest_dataset_test)
+  }
+  if (has_rfx) {
+    rm(rfx_dataset_train, rfx_tracker_train, rfx_model)
+  }
+  rm(outcome_train)
+  rm(rng)
+
+  # Restore global RNG state if user provided a random seed
+  if (custom_rng) {
+    .Random.seed <- original_global_seed
+  }
 
   return(result)
 }

R/bcf.R

@@ -340,16 +340,16 @@ bcf <- function(
   keep_vars_variance <- variance_forest_params_updated$keep_vars
   drop_vars_variance <- variance_forest_params_updated$drop_vars
   num_features_subsample_variance <- variance_forest_params_updated$num_features_subsample
-    
-    # Set a function-scoped RNG if user provided a random seed
-    custom_rng <- random_seed >= 0
-    if (custom_rng) {
-        # Store original global environment RNG state
-        original_global_seed <- .Random.seed
-        # Set new seed and store associated RNG state
-        set.seed(random_seed)
-        function_scoped_seed <- .Random.seed
-    }
+
+  # Set a function-scoped RNG if user provided a random seed
+  custom_rng <- random_seed >= 0
+  if (custom_rng) {
+    # Store original global environment RNG state
+    original_global_seed <- .Random.seed
+    # Set new seed and store associated RNG state
+    set.seed(random_seed)
+    function_scoped_seed <- .Random.seed
+  }
 
   # Check if there are enough GFR samples to seed num_chains samplers
   if (num_gfr > 0) {
@@ -2558,11 +2558,11 @@ bcf <- function(
     result[["bart_propensity_model"]] = bart_model_propensity
   }
   class(result) <- "bcfmodel"
-    
-    # Restore global RNG state if user provided a random seed
-    if (custom_rng) {
-        .Random.seed <- original_global_seed
-    }
+
+  # Restore global RNG state if user provided a random seed
+  if (custom_rng) {
+    .Random.seed <- original_global_seed
+  }
 
   return(result)
 }