R/rewind.R

In oslerinhealth/rewind: Reconstructing Etiology with Binary Decomposition

#' rewind: \strong{R}constructing \strong{E}tiology \strong{w}ith
#' B\strong{in}ary \strong{D}ecomposition
#'
#' \code{rewind} is designed for analyzing multivariate binary data via
#' binary factor analyses, with or without pre-specified number of factors,
#' and without specifying the number of clusters in the data. It is motivated by analyzing the multivariate presence or absence
#' of a list of antigens in serum samples collected from autoimmune disease patients.
#' This package provides a tool for statistical inference of a model that assumes human body generates antibodies to
#' a small number of protein complexes (or, machines), each comprised of a few important antigens.
#'
#' \code{rewind} This package implements a Bayesian hierarchical model that represents
#' observations as aggregation of a few unobserved binary machines where the aggregation
#' varies by subjects. Our approach is to specify the model likelihood via factorization
#' into two latent binary matrices: machine profiles and individual factors.
#' Given latent factorization, we account for inherent errors in measurement using
#' sensitivities and specificities of protein detection.
#' We use a prior for the individual factor matrix (Indian Buffet Process for binary matrices) to
#' encourage a small number of subject clusters each with distinct patterns of active machines.
#' The posterior distribution for the numbers of patient clusters and machines are
#' estimated from data and by design tend to concentrate on smaller values.
#' The posterior distributions of model parameters are estimated via Markov chain
#' Monte Carlo which makes a list of molecular machine profiles with uncertainty
#' quantification as well as patient-specific posterior probability of having each machine.
#'
#' @seealso
#' \itemize{
#' \item \url{https://github.com/zhenkewu/rewind} for the source code
#' and system/software requirements to use \code{rewind} for your data.
#' }
#'
#' @section Main rewind functions:
#' \code{\link{sampler}}
#'
#' @useDynLib rewind
#' @importFrom Rcpp sourceCpp
#' @docType package
#' @name rewind
#' @references
#' \itemize{
#' \item This package partly adapts Julia programs used in
#' Miller, J.W. and Harrison, M.T., 2017. Mixture models with a prior on the number of components.
#' Journal of the American Statistical Association, pp.1-17. \url{https://github.com/jwmi/BayesianMixtures.jl}
#' \item Slice sampler for Indian Buffet Process priors: Teh, Y. W., Grur, D., and Ghahramani, Z. (2007).
#' Stick-breaking construction for the indian buffet process. In Artificial Intelligence and Statistics, pages 556–563.
#' }
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