sbart: Sequential BART for Imputation of Missing Covariates

Implements the sequential BART (Bayesian Additive Regression Trees) approach to impute the missing covariates. The algorithm applies a Bayesian nonparametric approach on factored sets of sequential conditionals of the joint distribution of the covariates and the missingness and applying the Bayesian additive regression trees to model each of these univariate conditionals. Each conditional distribution is then sampled using MCMC algorithm. The published journal can be found at <> Package provides a function, seqBART(), which computes and returns the imputed values.

Version: 0.1.1
Depends: R (≥ 2.10)
Imports: LaplacesDemon, msm, Rcpp
LinkingTo: Rcpp
Suggests: testthat
Published: 2018-05-01
Author: Michael Daniels
Maintainer: Aarti Singh <mdstat2016 at>
License: MIT + file LICENSE
NeedsCompilation: yes
Materials: README NEWS
In views: MissingData
CRAN checks: sbart results


Reference manual: sbart.pdf
Package source: sbart_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: sbart_0.1.1.tgz, r-oldrel: sbart_0.1.1.tgz
Old sources: sbart archive

Reverse dependencies:

Reverse suggests: BART


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