#' Example Dataset
#'
#' A toy dataset to demonstrate running this package on multimodal linear models.
#'
#' @format A data object that contains
#' \describe{
#'   \item{\code{y}}{A vector of 200 observations of continuous outcomes.}
#'   \item{\code{X}}{A 200 \eqn{\times} 600 matrix containing all training data.}
#'   \item{\code{mod.idx}}{A list of modality indices.}
#' }
"data_linear_model"


#' Example Dataset
#'
#' A toy dataset to demonstrate running this package on multimodal logistic models.
#'
#' @format A data object that contains
#' \describe{
#'   \item{\code{y}}{A vector 200 observations of outcomes. (0 or 1)}
#'   \item{\code{X}}{A 200 \eqn{\times} 600 matrix containing all training data.}
#'   \item{\code{mod.idx}}{A list of modality indices.}
#' }
"data_logistic_model"


#' Example Dataset
#'
#' A toy dataset to demonstrate running this package on multimodal Cox
#' proportional hazards models. Survival times follow a Cox model with
#' independent right censoring (about 27\% of the observations are censored).
#'
#' @format A data object that contains
#' \describe{
#'   \item{\code{y}}{A 200 \eqn{\times} 2 matrix of survival outcomes, with columns
#'   \code{time} (the observed follow-up time) and \code{status} (the event
#'   indicator: 1 = event, 0 = censored). It may be passed directly to
#'   \code{\link{mglm.test}}, or converted with \code{\link[survival]{Surv}}.}
#'   \item{\code{X}}{A 200 \eqn{\times} 800 matrix containing all training data.}
#'   \item{\code{mod.idx}}{A list of modality indices: columns 1--400 form
#'   modality 1 and columns 401--800 form modality 2.}
#' }
"data_cox_model"


#' Example Dataset
#'
#' A toy dataset to demonstrate robust (Median-of-Means) estimation on
#' multimodal Negative-Binomial models with heavy-tailed responses. The counts
#' are strongly overdispersed (variance-to-mean ratio near 19, with roughly
#' half of the observations equal to zero).
#'
#' @format A data object that contains
#' \describe{
#'   \item{\code{y}}{A vector of 300 observations of non-negative counts.}
#'   \item{\code{X}}{A 300 \eqn{\times} 600 matrix containing all training data.}
#'   \item{\code{mod.idx}}{A list of modality indices: columns 1--200, 201--400,
#'   and 401--600 form modalities 1, 2, and 3.}
#'   \item{\code{theta}}{The true Negative-Binomial dispersion used to generate
#'   the counts, \eqn{\theta = 0.5}. It may be supplied to \code{\link{mglm.test}}
#'   through the \code{theta} argument; otherwise it is estimated from the data.}
#' }
"data_nb_model"
