#' Modeling functions available in \pkg{rstanarm}
#'
#' @name available-models
#'
#' @section Modeling functions:
#' The model estimating functions are described in greater detail in their
#' individual help pages and vignettes. Here we provide a very brief
#' overview:
#'
#' \describe{
#' \item{\code{\link{stan_lm}}, \code{stan_aov}, \code{stan_biglm}}{
#' Similar to \code{\link[stats]{lm}} or \code{\link[stats]{aov}} but with
#' novel regularizing priors on the model parameters that are driven by prior
#' beliefs about \eqn{R^2}, the proportion of variance in the outcome
#' attributable to the predictors in a linear model.
#' }
#' \item{\code{\link{stan_glm}}, \code{stan_glm.nb}}{
#' Similar to \code{\link[stats]{glm}} but with various possible prior
#' distributions for the coefficients and, if applicable, a prior distribution
#' for any auxiliary parameter in a Generalized Linear Model (GLM) that is
#' characterized by a \code{\link[stats]{family}} object (e.g. the shape
#' parameter in Gamma models). It is also possible to estimate a negative
#' binomial model in a similar way to the \code{\link[MASS]{glm.nb}} function
#' in the \pkg{MASS} package.
#' }
#' \item{\code{\link{stan_glmer}}, \code{stan_glmer.nb}, \code{stan_lmer}}{
#' Similar to the \code{\link[lme4]{glmer}}, \code{\link[lme4]{glmer.nb}} and
#' \code{\link[lme4]{lmer}} functions in the \pkg{lme4} package in that GLMs
#' are augmented to have group-specific terms that deviate from the common
#' coefficients according to a mean-zero multivariate normal distribution with
#' a highly-structured but unknown covariance matrix (for which \pkg{rstanarm}
#' introduces an innovative prior distribution). MCMC provides more
#' appropriate estimates of uncertainty for models that consist of a mix of
#' common and group-specific parameters.
#' }
#' \item{\code{\link{stan_nlmer}}}{
#' Similar to \code{\link[lme4]{nlmer}} in the \pkg{lme4} package for
#' nonlinear "mixed-effects" models, but the group-specific coefficients
#' have flexible priors on their unknown covariance matrices.
#' }
#' \item{\code{\link{stan_gamm4}}}{
#' Similar to \code{\link[gamm4]{gamm4}} in the \pkg{gamm4} package, which
#' augments a GLM (possibly with group-specific terms) with nonlinear smooth
#' functions of the predictors to form a Generalized Additive Mixed Model
#' (GAMM). Rather than calling \code{\link[lme4]{glmer}} like
#' \code{\link[gamm4]{gamm4}} does, \code{\link{stan_gamm4}} essentially calls
#' \code{\link{stan_glmer}}, which avoids the optimization issues that often
#' crop up with GAMMs and provides better estimates for the uncertainty of the
#' parameter estimates.
#' }
#' \item{\code{\link{stan_polr}}}{
#' Similar to \code{\link[MASS]{polr}} in the \pkg{MASS} package in that it
#' models an ordinal response, but the Bayesian model also implies a prior
#' distribution on the unknown cutpoints. Can also be used to model binary
#' outcomes, possibly while estimating an unknown exponent governing the
#' probability of success.
#' }
#' \item{\code{\link{stan_betareg}}}{
#' Similar to \code{\link[betareg]{betareg}} in that it models an outcome that
#' is a rate (proportion) but, rather than performing maximum likelihood
#' estimation, full Bayesian estimation is performed by default, with
#' customizable prior distributions for all parameters.
#' }
#' \item{\code{\link{stan_clogit}}}{
#' Similar to \code{\link[survival]{clogit}} in that it models an binary outcome
#' where the number of successes and failures is fixed within each stratum by
#' the research design. There are some minor syntactical differences relative
#' to \code{\link[survival]{clogit}} that allow \code{stan_clogit} to accept
#' group-specific terms as in \code{\link{stan_glmer}}.
#' }
#' \item{\code{\link{stan_mvmer}}}{
#' A multivariate form of \code{\link{stan_glmer}}, whereby the user can
#' specify one or more submodels each consisting of a GLM with group-specific
#' terms. If more than one submodel is specified (i.e. there is more than one
#' outcome variable) then a dependence is induced by assuming that the
#' group-specific terms for each grouping factor are correlated across submodels.
#' }
#' \item{\code{\link{stan_jm}}}{
#' Estimates shared parameter joint models for longitudinal and time-to-event
#' (i.e. survival) data. The joint model can be univariate (i.e. one longitudinal
#' outcome) or multivariate (i.e. more than one longitudinal outcome). A variety
#' of parameterisations are available for linking the longitudinal and event
#' processes (i.e. a variety of association structures).
#' }
#' }
#'
#' @seealso \url{https://mc-stan.org/rstanarm/}
#'
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