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#' @templateVar MODEL_FUNCTION choiceRT_ddm_single
#' @templateVar CONTRIBUTOR
#' @templateVar TASK_NAME Choice Reaction Time Task
#' @templateVar TASK_CODE choiceRT
#' @templateVar TASK_CITE
#' @templateVar MODEL_NAME Drift Diffusion Model
#' @templateVar MODEL_CODE ddm
#' @templateVar MODEL_CITE (Ratcliff, 1978)
#' @templateVar MODEL_TYPE Individual
#' @templateVar DATA_COLUMNS "subjID", "choice", "RT"
#' @templateVar PARAMETERS \code{alpha} (boundary separation), \code{beta} (bias), \code{delta} (drift rate), \code{tau} (non-decision time)
#' @templateVar REGRESSORS
#' @templateVar POSTPREDS
#' @templateVar LENGTH_DATA_COLUMNS 3
#' @templateVar DETAILS_DATA_1 \item{subjID}{A unique identifier for each subject in the data-set.}
#' @templateVar DETAILS_DATA_2 \item{choice}{Choice made for the current trial, coded as 1/2 to indicate lower/upper boundary or left/right choices (e.g., 1 1 1 2 1 2).}
#' @templateVar DETAILS_DATA_3 \item{RT}{Choice reaction time for the current trial, in **seconds** (e.g., 0.435 0.383 0.314 0.309, etc.).}
#' @templateVar LENGTH_ADDITIONAL_ARGS 1
#' @templateVar ADDITIONAL_ARGS_1 \item{RTbound}{Floating point value representing the lower bound (i.e., minimum allowed) reaction time. Defaults to 0.1 (100 milliseconds).}
#'
#' @template model-documentation
#'
#' @export
#' @include hBayesDM_model.R
#' @include preprocess_funcs.R
#' #' @note
#' \strong{Notes:}
#' Note that this implementation is NOT the full Drift Diffusion Model as described in Ratcliff (1978). This implementation estimates the drift rate, boundary separation, starting point, and non-decision time; but not the between- and within-trial variances in these parameters.
#' Code for this model is based on codes/comments by Guido Biele, Joseph Burling, Andrew Ellis, and potential others @ Stan mailing.
#'
#' @references
#' Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59-108. https://doi.org/10.1037/0033-295X.85.2.59
#'
choiceRT_ddm_single <- hBayesDM_model(
task_name = "choiceRT",
model_name = "ddm",
model_type = "single",
data_columns = c("subjID", "choice", "RT"),
parameters = list(
"alpha" = c(0, 0.5, Inf),
"beta" = c(0, 0.5, 1),
"delta" = c(-Inf, 0, Inf),
"tau" = c(0, 0.15, 1)
),
regressors = NULL,
postpreds = NULL,
preprocess_func = choiceRT_single_preprocess_func)
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