R/binaryRL-package.R

In binaryRL: Reinforcement Learning Tools for Two-Alternative Forced Choice Tasks

#' @keywords internal
"_PACKAGE"
#' @aliases binaryRL
#' 
#' @title binaryRL: Reinforcement Learning Tools for Two-Alternative Forced Choice Tasks.
#'
#' @section Example Data:
#'  \itemize{
#'    \item \code{\link[binaryRL]{Mason_2024_G1}}: 
#'      Group 1 of Mason et al. (2024)
#'    \item \code{\link[binaryRL]{Mason_2024_G2}}: 
#'      Group 2 of Mason et al. (2024)
#' }
#'
#' @section Steps:
#'  \itemize{
#'    \item \code{\link[binaryRL]{run_m}}: 
#'      Step 1: Building reinforcement learning model
#'    \item \code{\link[binaryRL]{rcv_d}}: 
#'      Step 2: Generating fake data for parameter and model recovery
#'    \item \code{\link[binaryRL]{fit_p}}: 
#'      Step 3: Optimizing parameters to fit real data
#'    \item \code{\link[binaryRL]{rpl_e}}: 
#'      Step 4: Replaying the experiment with optimal parameters
#' }
#' 
#' @section Models:
#'  \itemize{
#'    \item \code{\link[binaryRL]{TD}}: 
#'      TD Model
#'    \item \code{\link[binaryRL]{RSTD}}: 
#'      RSTD Model
#'    \item \code{\link[binaryRL]{Utility}}: 
#'      Utility Model
#' }
#' 
#' @section Functions:
#' \itemize{
#'   \item \code{\link[binaryRL]{func_gamma}}: 
#'    Utility Function
#'   \item \code{\link[binaryRL]{func_eta}}: 
#'    Learning Rate
#'   \item \code{\link[binaryRL]{func_epsilon}}: 
#'    Epsilon Related
#'   \item \code{\link[binaryRL]{func_pi}}: 
#'    Upper-Confidence-Bound
#'   \item \code{\link[binaryRL]{func_tau}}: 
#'    Soft-Max
#'   \item \code{\link[binaryRL]{func_logl}}: 
#'    Loss Function
#' }
#' 
#' @section Processes: 
#'  \itemize{
#'    \item \code{\link[binaryRL]{optimize_para}}: 
#'      optimizing free parameters
#'    \item \code{\link[binaryRL]{simulate_list}}: 
#'      simulating fake datasets
#'    \item \code{\link[binaryRL]{recovery_data}}: 
#'      parameter and model recovery
#' }
#'
#' @section Summary:
#' \itemize{
#'   \item \code{\link[binaryRL]{summary.binaryRL}}: summary(binaryRL.res)
#' }
#'
#' @name binaryRL-package
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