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#' @templateVar MODEL_FUNCTION rdt_happiness
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/harhim-park/}{Harhim Park} <\email{hrpark12@@gmail.com}>
#' @templateVar TASK_NAME Risky Decision Task
#' @templateVar TASK_CODE rdt
#' @templateVar TASK_CITE
#' @templateVar MODEL_NAME Happiness Computational Model
#' @templateVar MODEL_CODE happiness
#' @templateVar MODEL_CITE (Rutledge et al., 2014)
#' @templateVar MODEL_TYPE Hierarchical
#' @templateVar DATA_COLUMNS "subjID", "gain", "loss", "cert", "type", "gamble", "outcome", "happy", "RT_happy"
#' @templateVar PARAMETERS \code{w0} (baseline), \code{w1} (weight of certain rewards), \code{w2} (weight of expected values), \code{w3} (weight of reward prediction errors), \code{gam} (forgetting factor), \code{sig} (standard deviation of error)
#' @templateVar REGRESSORS
#' @templateVar POSTPREDS "y_pred"
#' @templateVar LENGTH_DATA_COLUMNS 9
#' @templateVar DETAILS_DATA_1 \item{subjID}{A unique identifier for each subject in the data-set.}
#' @templateVar DETAILS_DATA_2 \item{gain}{Possible (50\%) gain outcome of a risky option (e.g. 9).}
#' @templateVar DETAILS_DATA_3 \item{loss}{Possible (50\%) loss outcome of a risky option (e.g. 5, or -5).}
#' @templateVar DETAILS_DATA_4 \item{cert}{Guaranteed amount of a safe option.}
#' @templateVar DETAILS_DATA_5 \item{type}{loss == -1, mixed == 0, gain == 1}
#' @templateVar DETAILS_DATA_6 \item{gamble}{If gamble was taken, gamble == 1; else gamble == 0.}
#' @templateVar DETAILS_DATA_7 \item{outcome}{Result of the trial.}
#' @templateVar DETAILS_DATA_8 \item{happy}{Happiness score.}
#' @templateVar DETAILS_DATA_9 \item{RT_happy}{Reaction time for answering the happiness score.}
#' @templateVar LENGTH_ADDITIONAL_ARGS 0
#'
#' @template model-documentation
#'
#' @export
#' @include hBayesDM_model.R
#' @include preprocess_funcs.R
#' @references
#' Rutledge, R. B., Skandali, N., Dayan, P., & Dolan, R. J. (2014). A computational and neural model of momentary subjective well-being. Proceedings of the National Academy of Sciences, 111(33), 12252-12257.
#'
rdt_happiness <- hBayesDM_model(
task_name = "rdt",
model_name = "happiness",
model_type = "",
data_columns = c("subjID", "gain", "loss", "cert", "type", "gamble", "outcome", "happy", "RT_happy"),
parameters = list(
"w0" = c(-Inf, 1, Inf),
"w1" = c(-Inf, 1, Inf),
"w2" = c(-Inf, 1, Inf),
"w3" = c(-Inf, 1, Inf),
"gam" = c(0, 0.5, 1),
"sig" = c(0, 1, Inf)
),
regressors = NULL,
postpreds = c("y_pred"),
preprocess_func = rdt_preprocess_func)
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