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#' @templateVar MODEL_FUNCTION dd_cs
#' @templateVar CONTRIBUTOR
#' @templateVar TASK_NAME Delay Discounting Task
#' @templateVar TASK_CODE dd
#' @templateVar TASK_CITE
#' @templateVar MODEL_NAME Constant-Sensitivity (CS) Model
#' @templateVar MODEL_CODE cs
#' @templateVar MODEL_CITE (Ebert et al., 2007)
#' @templateVar MODEL_TYPE Hierarchical
#' @templateVar DATA_COLUMNS "subjID", "delay_later", "amount_later", "delay_sooner", "amount_sooner", "choice"
#' @templateVar PARAMETERS \code{r} (exponential discounting rate), \code{s} (impatience), \code{beta} (inverse temperature)
#' @templateVar REGRESSORS
#' @templateVar POSTPREDS "y_pred"
#' @templateVar LENGTH_DATA_COLUMNS 6
#' @templateVar DETAILS_DATA_1 \item{subjID}{A unique identifier for each subject in the data-set.}
#' @templateVar DETAILS_DATA_2 \item{delay_later}{An integer representing the delayed days for the later option (e.g. 1, 6, 28).}
#' @templateVar DETAILS_DATA_3 \item{amount_later}{A floating point number representing the amount for the later option (e.g. 10.5, 13.4, 30.9).}
#' @templateVar DETAILS_DATA_4 \item{delay_sooner}{An integer representing the delayed days for the sooner option (e.g. 0).}
#' @templateVar DETAILS_DATA_5 \item{amount_sooner}{A floating point number representing the amount for the sooner option (e.g. 10).}
#' @templateVar DETAILS_DATA_6 \item{choice}{If amount_later was selected, choice == 1; else if amount_sooner was selected, choice == 0.}
#' @templateVar LENGTH_ADDITIONAL_ARGS 0
#'
#' @template model-documentation
#'
#' @export
#' @include hBayesDM_model.R
#' @include preprocess_funcs.R
#' @references
#' Ebert, J. E. J., & Prelec, D. (2007). The Fragility of Time: Time-Insensitivity and Valuation of the Near and Far Future. Management Science. https://doi.org/10.1287/mnsc.1060.0671
#'
dd_cs <- hBayesDM_model(
task_name = "dd",
model_name = "cs",
model_type = "",
data_columns = c("subjID", "delay_later", "amount_later", "delay_sooner", "amount_sooner", "choice"),
parameters = list(
"r" = c(0, 0.1, 1),
"s" = c(0, 1, 10),
"beta" = c(0, 1, 5)
),
regressors = NULL,
postpreds = c("y_pred"),
preprocess_func = dd_preprocess_func)
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