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 <https://doi.org/10.1093/biostatistics/kxw009> Package provides a function, seqBART(), which computes and returns the imputed values.
| Version: | 0.1.0 |
| Depends: | R (≥ 2.10) |
| Imports: | LaplacesDemon, msm, Rcpp |
| LinkingTo: | Rcpp |
| Suggests: | testthat |
| Published: | 2017-03-23 |
| Author: | Michael Daniels |
| Maintainer: | Aarti Singh <mdstat2016 at gmail.com> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | sbart results |
| Reference manual: | sbart.pdf |
| Package source: | sbart_0.1.0.tar.gz |
| Windows binaries: | r-devel: sbart_0.1.0.zip, r-release: sbart_0.1.0.zip, r-oldrel: sbart_0.1.0.zip |
| OS X El Capitan binaries: | r-release: sbart_0.1.0.tgz |
| OS X Mavericks binaries: | r-oldrel: sbart_0.1.0.tgz |
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