for CRAN resubmission

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: RLoptimal
 Type: Package
 Title: Optimal Adaptive Allocation Using Deep Reinforcement Learning
-Version: 1.0.0
+Version: 1.0.1
 Authors@R: c(
     person("Kentaro", "Matsuura", , "[email protected]", 
            role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0001-5262-055X")),

R/adjust_significance_level.R

@@ -25,18 +25,31 @@
 #' @returns A positive numeric value specifying adjusted significance level.
 #' 
 #' @examples
-#' # We computed `allocation_rule`
-#' # ...
+#' library(RLoptimal)
 #' 
+#' doses <- c(0, 2, 4, 6, 8)
+#' 
+#' models <- DoseFinding::Mods(
+#'   doses = doses, maxEff = 1.65,
+#'   linear = NULL, emax = 0.79, sigEmax = c(4, 5)
+#' )
+#' 
+#' \dontrun{
+#' allocation_rule <- learn_allocation_rule(
+#'   models,
+#'   N_total = 150, N_ini = rep(10, 5), N_block = 10, Delta = 1.3,
+#'   outcome_type = "continuous", sd_normal = sqrt(4.5), 
+#'   seed = 123, rl_config = rl_config_set(iter = 1000),
+#'   alpha = 0.025
+#' )
+#'
 #' # Simulation-based adjustment of the significance level using `allocation_rule`
-#' \donttest{
 #' adjusted_alpha <- adjust_significance_level(
 #'   allocation_rule, models,
 #'   N_total = 150, N_ini = rep(10, 5), N_block = 10,
 #'   outcome_type = "continuous", sd_normal = sqrt(4.5),
 #'   alpha = 0.025, n_sim = 10000, seed = 123
-#' )
-#' }
+#' )}
 #'
 #' @importFrom stats quantile
 #'

R/learn_allocation_rule.R

@@ -65,15 +65,14 @@
 #' 
 #' # We obtain an optimal adaptive allocation rule by executing 
 #' # `learn_allocation_rule()` with the `models`.
-#' \donttest{
+#' \dontrun{
 #' allocation_rule <- learn_allocation_rule(
 #'   models,
 #'   N_total = 150, N_ini = rep(10, 5), N_block = 10, Delta = 1.3,
 #'   outcome_type = "continuous", sd_normal = sqrt(4.5), 
 #'   seed = 123, rl_config = rl_config_set(iter = 1000),
 #'   alpha = 0.025
-#' )
-#' }
+#' )}
 #' 
 #' # It is recommended that the models used in reinforcement learning include 
 #' # possible models in addition to the models used in the MCPMod method. 
@@ -85,15 +84,14 @@
 #' )
 #' 
 #' # Then, we specify the argument `rl_models` in `learn_allocation_rule` function.
-#' \donttest{
+#' \dontrun{
 #' allocation_rule <- learn_allocation_rule(
 #'   models,
 #'   N_total = 150, N_ini = rep(10, 5), N_block = 10, Delta = 1.3,
 #'   outcome_type = "continuous", sd_normal = sqrt(4.5), 
 #'   seed = 123, rl_models = rl_models, rl_config = rl_config_set(iter = 1000),
 #'   alpha = 0.025
-#' )
-#' }
+#' )}
 #' 
 #' @importFrom glue glue
 #'

R/rl_config_set.R

@@ -27,7 +27,7 @@
 #' @return A list of reinforcement learning configuration parameters
 #' 
 #' @examples
-#' \donttest{
+#' \dontrun{
 #' allocation_rule <- learn_allocation_rule(
 #'   models, 
 #'   N_total = 150, N_ini = rep(10, 5), N_block = 10, Delta = 1.3,
@@ -36,8 +36,7 @@
 #'   # We change `iter` to 200 and `cores` for reinforcement learning to 2
 #'   rl_config = rl_config_set(iter = 200, cores = 2), 
 #'   alpha = 0.025
-#' )
-#' } 
+#' )} 
 #'
 #' @export
 rl_config_set <- function(iter = 1000L, 

R/rl_dnn_config.R

@@ -14,7 +14,7 @@
 #' @return A list of DNN configuration parameters
 #' 
 #' @examples
-#' \donttest{
+#' \dontrun{
 #' allocation_rule <- learn_allocation_rule(
 #'   models, 
 #'   N_total = 150, N_ini = rep(10, 5), N_block = 10, Delta = 1.3,
@@ -27,8 +27,7 @@
 #'     model = rl_dnn_config(fcnet_hiddens = c(512L, 512L), fcnet_activation = "tanh")
 #'   ), 
 #'   alpha = 0.025
-#' )
-#' } 
+#' )} 
 #'
 #' @export
 rl_dnn_config <- function(

R/simulate_one_trial.R

@@ -37,9 +37,31 @@
 #'        the estimated target dose, and the MAE.
 #' 
 #' @examples
-#' \donttest{  
-#' # We computed `allocation_rule` and `adjusted_alpha`
-#' # ...
+#' library(RLoptimal)
+#' 
+#' doses <- c(0, 2, 4, 6, 8)
+#' 
+#' models <- DoseFinding::Mods(
+#'   doses = doses, maxEff = 1.65,
+#'   linear = NULL, emax = 0.79, sigEmax = c(4, 5)
+#' )
+#' 
+#' \dontrun{
+#' allocation_rule <- learn_allocation_rule(
+#'   models,
+#'   N_total = 150, N_ini = rep(10, 5), N_block = 10, Delta = 1.3,
+#'   outcome_type = "continuous", sd_normal = sqrt(4.5), 
+#'   seed = 123, rl_config = rl_config_set(iter = 1000),
+#'   alpha = 0.025
+#' )
+#'
+#' # Simulation-based adjustment of the significance level using `allocation_rule`
+#' adjusted_alpha <- adjust_significance_level(
+#'   allocation_rule, models,
+#'   N_total = 150, N_ini = rep(10, 5), N_block = 10,
+#'   outcome_type = "continuous", sd_normal = sqrt(4.5),
+#'   alpha = 0.025, n_sim = 10000, seed = 123
+#' )}
 #' 
 #' eval_models <- DoseFinding::Mods(
 #'   doses = doses, maxEff = 1.65,
@@ -51,14 +73,14 @@
 #' true_model_name <- "emax"
 #' 
 #' # Simulate one trial using the obtained `allocation_rule` When the true model is "emax"
+#' \dontrun{
 #' res_one <- simulate_one_trial(
 #'   allocation_rule, models, 
 #'   true_response = true_response_list[[true_model_name]],
 #'   N_total = 150, N_ini = rep(10, 5), N_block = 10, 
 #'   Delta = 1.3, outcome_type = "continuous", sd_normal = sqrt(4.5),
 #'   alpha = adjusted_alpha, seed = simID, eval_type = "all"
-#' )
-#' }
+#' )}
 #' 
 #' @importFrom stats coef binomial glm plogis predict rbinom rnorm vcov
 #'