diff --git a/.Rbuildignore b/.Rbuildignore
index f01373a..af4efa0 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -16,3 +16,4 @@
 ^\.github$
 ^README\.Rmd$
 ^vignettes/articles$
+^tests/testthat/test-ALEPlot.R$
diff --git a/R/ale_core.R b/R/ale_core.R
index 07befe1..5e1d94f 100644
--- a/R/ale_core.R
+++ b/R/ale_core.R
@@ -360,6 +360,7 @@ ale <- function (
     },
     pred_type = "response",
     p_values = NULL,
+    p_alpha = c(0.01, 0.05),
     x_intervals = 100,
     boot_it = 0,
     seed = 0,
diff --git a/R/stats.R b/R/stats.R
index 67b0928..f4ba026 100644
--- a/R/stats.R
+++ b/R/stats.R
@@ -234,9 +234,9 @@ create_p_funs <- function(
       )
     )
     assert_that(
-      stringr::str_detect(random_model_call_string, '.rand_train'),
+      stringr::str_detect(random_model_call_string, 'rand_train'),
       msg = glue(
-        'The data argument for random_model_call_string must be ".rand_train". ',
+        'The data argument for random_model_call_string must be "rand_train". ',
         'See help(create_p_funs) for details.'
       )
     )
@@ -274,9 +274,9 @@ create_p_funs <- function(
 
   # This super-assignment might be problematic. See this tip from ChatGPT to resolve it:
   # https://chat.openai.com/c/08b68562-c339-4c37-baab-3c71d2e9fb73
-  .rand_model <<- NULL
-  .rand_test <<- NULL
-  .rand_train <<- NULL
+  rand_model <<- NULL
+  rand_test <<- NULL
+  rand_train <<- NULL
 
 
 
@@ -289,8 +289,8 @@ create_p_funs <- function(
 
   # Create ALEs for random variables based on residual_distribution
   # Super-assignment so that the modified datasets will be visible within the ale function
-  .rand_train <<- training_data
-  .rand_test  <<- test_data
+  rand_train <<- training_data
+  rand_test  <<- test_data
   train_n <- nrow(training_data)
   test_n  <- nrow(test_data)
 
@@ -316,14 +316,14 @@ create_p_funs <- function(
   }
 
 
-  # .rand_ales <- map(
-  # .rand_ales <- furrr::future_map(
+  # rand_ales <- map(
+  # rand_ales <- furrr::future_map(
   # extend random_model_call_string_vars with local variables for parallel processing
   random_model_call_string_vars <- c(
-    '.rand_train', '.rand_test', '.random_variable', '.rand_model', '.rand_ale',
+    'rand_train', 'rand_test', '.random_variable', 'rand_model', 'rand_ale',
     random_model_call_string_vars
   )
-  .rand_ales <- map_loop(
+  rand_ales <- map_loop(
     # .progress = !silent,  # future_map does not allow messages for .progress
     .options = furrr::furrr_options(
       # Enable parallel-processing random seed generation
@@ -356,38 +356,38 @@ create_p_funs <- function(
       # Generate training and test subsets with the random variable.
       # Super-assignment because they modify the datasets defined outside of the map function.
       set.seed(.it)
-      .rand_train$.random_variable <<- univariateML::rml(
+      rand_train$.random_variable <<- univariateML::rml(
         n = train_n,
         obj = residual_distribution
       )
-      .rand_test$.random_variable <<- univariateML::rml(
+      rand_test$.random_variable <<- univariateML::rml(
         n = test_n,
         obj = residual_distribution
       )
 
       # Train model with the random variable: convert model call string to an expression
-      # Super-assignment so that .rand_model will be visible within the ale function
+      # Super-assignment so that rand_model will be visible within the ale function
 
       # If random_model_call_string was provided, prefer it to automatic detection
       if (!is.null(random_model_call_string)) {
-        .rand_model <<- random_model_call_string |>
+        rand_model <<- random_model_call_string |>
           parse(text = _) |>
           eval()
       }
       else {  # use the automatically detected model call
-        # Update the model to call to add .random_variable and to train on .rand_train
+        # Update the model to call to add .random_variable and to train on rand_train
         model_call$formula <- model_call$formula |>
           stats::update.formula(~ . + .random_variable)
-        model_call$data <- .rand_train
+        model_call$data <- rand_train
 
-        .rand_model <<- eval(model_call)
+        rand_model <<- eval(model_call)
       }
 
       # Calculate ale of random variable on the test set.
       # If calculated on the training set, p-values will be too liberal.
-      .rand_ale <- ale::ale(
-        .rand_test,
-        .rand_model,
+      rand_ale <- ale::ale(
+        rand_test,
+        rand_model,
         '.random_variable',
         # avoid iterative parallelization
         parallel = 0,
@@ -399,7 +399,7 @@ create_p_funs <- function(
         relative_y = 'zero'
       )
 
-      .rand_ale
+      rand_ale
     })
 
   # Disable parallel processing if it had been enabled
@@ -415,7 +415,7 @@ create_p_funs <- function(
   # ale_y_norm_fun <- create_ale_y_norm_function(test_data[[y_col]])
 
   rand_stats <-
-    map(.rand_ales, \(.rand) {
+    map(rand_ales, \(.rand) {
       ale_stats(
         ale_y = .rand$data$.random_variable$ale_y,
         ale_n = .rand$data$.random_variable$ale_n,
diff --git a/man/ale.Rd b/man/ale.Rd
index 2cb6674..d3c7669 100644
--- a/man/ale.Rd
+++ b/man/ale.Rd
@@ -19,6 +19,7 @@ ale(
  },
   pred_type = "response",
   p_values = NULL,
+  p_alpha = c(0.01, 0.05),
   x_intervals = 100,
   boot_it = 0,
   seed = 0,
@@ -84,6 +85,17 @@ will result in an error. For more details about creating p-values,
 see documentation for \code{\link[=create_p_funs]{create_p_funs()}}. Note that p-values will not be
 generated if 'stats' are not included as an option in the \code{output} argument.}
 
+\item{p_alpha}{numeric length 2 from 0 to 1. Alpha for "confidence interval" ranges
+for printing bands around the median for single-variable plots. These are the
+default values used if \code{p_values} are provided. If \code{p_values} are not provided,
+then \code{median_band_pct} is used instead.
+The inner band range will be the median value of y ± \code{p_alpha[2]} of the relevant
+ALE statistic (usually ALE range or normalized ALE range).
+For plots with a second outer band, its range will be the median ± \code{p_alpha[1]}.
+For example, in the ALE plots, for the default \code{p_alpha = c(0.01, 0.05)},
+the inner band will be the median ± ALE minimum or maximum at p = 0.05 and
+the outer band will be the median ± ALE minimum or maximum at p = 0.01.}
+
 \item{x_intervals}{positive integer length 1. Maximum number of intervals on the x-axis
 for the ALE data for each column in \code{x_cols}. The number of intervals that the algorithm generates
 might eventually be fewer than what the user specifies if the data values for
@@ -143,17 +155,6 @@ see the \code{\link[=model_bootstrap]{model_bootstrap()}} function).}
 non-essential messages during execution (such as progress bars).
 Regardless, any warnings and errors will always display. See details for how
 to enable progress bars.}
-
-\item{p_alpha}{numeric length 2 from 0 to 1. Alpha for "confidence interval" ranges
-for printing bands around the median for single-variable plots. These are the
-default values used if \code{p_values} are provided. If \code{p_values} are not provided,
-then \code{median_band_pct} is used instead.
-The inner band range will be the median value of y ± \code{p_alpha[2]} of the relevant
-ALE statistic (usually ALE range or normalized ALE range).
-For plots with a second outer band, its range will be the median ± \code{p_alpha[1]}.
-For example, in the ALE plots, for the default \code{p_alpha = c(0.01, 0.05)},
-the inner band will be the median ± ALE minimum or maximum at p = 0.05 and
-the outer band will be the median ± ALE minimum or maximum at p = 0.01.}
 }
 \value{
 list with the following elements: