R/smirf-package.R

In stephematician/miForang: Single or multiple imputation of missing data using random forests

#' \pkg{smirf}: Single or multiple imputation of missing data using random forests
#'
#' This code is licensed under the MIT license
#' \url{https://www.r-project.org/Licenses/MIT}, and you may use this package
#' strictly under those terms
#'
#' @references
#'
#' Stekhoven, D.J. and Buehlmann, P., 2012. MissForest--non-parametric
#' missing value imputation for mixed-type data. \emph{Bioinformatics, 28}(1),
#' pp. 112-118.
#' \href{https://dx.doi.org/10.1093/bioinformatics/btr597}{
#'   doi.1.1093/bioinformatics/btr597
#' }
#'
#' Van Buuren, S., Brand, J.P., Groothuis-Oudshoorn, C.G. and Rubin, D.B., 2006.
#' Fully conditional specification in multivariate imputation. \emph{Journal of
#' statistical computation and simulation, 76}(12), pp.1049-1064.
#' \href{https://dx.doi.org/10.1080/10629360600810434}{
#'   doi.10.1080/10629360600810434
#' }
#'
#' Wright, M. N. and Ziegler, A., 2017. ranger: A fast implementation of random
#' forests for high dimensional data in C++ and R. \emph{Journal of Statistical
#' Software, 77}(i01), pp. 1-17.
#' \href{https://dx.doi.org/10.18637/jss.v077.i01}{
#'   doi.10.18637/jss.v077.i01
#' }
#'
#' @author Stephen Wade \email{stephematician@@gmail.com}
#'
#' @importFrom stats setNames cor predict
#' @importFrom utils combn
#' @import rlang
#'
#' @docType package
#' @name smirf-package
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