stride: STRIDE: Robust powerful mixture models

#' stride package
#'
#' This package provides functions to nonparametrically estimate survival curves
#' from mixture data. Mixture data is when data are collected from multiple populations
#' of interest, but no information is provided as to which population each observation belongs.
#' The functions are for nonparametric prediction models that incorporate covariates and landmarking.
#'
#'@references
#'Garcia, T.P. and Parast, L. (2020). Dynamic landmark prediction for mixture data. Biostatistics,  doi:10.1093/biostatistics/kxz052.
#'
#'Garcia, T.P., Marder, K. and Wang, Y. (2017). Statistical modeling of Huntington disease onset.
#'In Handbook of Clinical Neurology, vol 144, 3rd Series, editors Andrew Feigin and Karen E. Anderson.
#'
#'Qing, J., Garcia, T.P., Ma, Y., Tang, M.X., Marder, K., and Wang, Y. (2014).
#'Combining isotonic regression and EM algorithm to predict genetic risk under monotonicity constraint.
#'Annals of Applied Statistics, 8(2), 1182-1208.
#'
#'Wang, Y., Garcia, T.P., and Ma. Y. (2012).  Nonparametric estimation for censored mixture data with
#'application to the Cooperative Huntington's Observational Research Trial. Journal of the American Statistical Association,
#'107, 1324-1338.
#'
#' @docType package
#' @name stride
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