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#' Helper function to parse distributional formulas and return components for fitting a brms model
#'
#' @param model a model passed from growthSS
#' @param x the x variable
#' @param y the response variable
#' @param group the grouping variable
#' @param sigma Logical, is this a distributional parameter? Defaults to TRUE here.
#' @param nTimes Number of unique x values. Used for splines, if nTimes is too low then the spline knots
#' must be adjusted. Defaults to 25.
#' @param useGroup Logical, should groups be used?
#' @param priors Priors in growthSS syntax, passed to .brmsChangePointHelper for thresholded models.
#' @param int logical, should an intercept be included in the model?
#' @param force_nl logical, should non-linear parameterization of a linear model be forced?
#'
#' @return A list of elements to pass brmSS for fitting distributional models
#'
#'
#' @keywords internal
#' @noRd
.brmDparHelper <- function(dpar, model, x, group, nTimes, useGroup, priors, int = FALSE,
force_nl = FALSE) {
splineDparHelperForm <- NULL
if (grepl("\\+", model)) {
chngptHelperList <- .brmsChangePointHelper(model, x,
y = dpar, group, dpar = TRUE,
nTimes, useGroup, priors, int = int
)
dparForm <- chngptHelperList$growthForm
dpar_pars <- chngptHelperList$pars
splineDparHelperForm <- chngptHelperList$splineHelperForm
} else {
if (model == "homo") {
model <- "int" # recode alternate names
} else if (model == "spline") {
model <- "gam"
}
stringBrmsDparFormFun <- paste0(".brms_form_", gsub(" ", "", model))
brmsDparFormFun <- match.fun(stringBrmsDparFormFun)
formResDpar <- brmsDparFormFun(x, dpar, group,
dpar = TRUE, nTimes = nTimes,
useGroup = useGroup, prior = priors, int = int, force_nl = force_nl
)
dparForm <- formResDpar$form
dpar_pars <- formResDpar$pars
}
return(list(
"dparForm" = dparForm, "dpar_pars" = dpar_pars,
"dparSplineHelperForm" = splineDparHelperForm
))
}
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