MixedDataImpute: Missing Data Imputation for Continuous and Categorical Data using Nonparametric Bayesian Joint Models

Missing data imputation for continuous and categorical data, using nonparametric Bayesian joint models (specifically the hierarchically coupled mixture model with local dependence described in Murray and Reiter (2015); see 'citation("MixedDataImpute")' or http://arxiv.org/abs/1410.0438). See '?hcmm_impute' for example usage.

Version: 0.1
Depends: gdata, Rcpp (≥ 0.11), R (≥ 3.1.0)
Imports: methods
LinkingTo: Rcpp, RcppArmadillo, BH
Published: 2016-02-07
Author: Jared S. Murray
Maintainer: Jared S. Murray <jsmurray at stat.cmu.edu>
License: GPL-3
NeedsCompilation: yes
Citation: MixedDataImpute citation info
In views: MissingData
CRAN checks: MixedDataImpute results


Reference manual: MixedDataImpute.pdf
Package source: MixedDataImpute_0.1.tar.gz
Windows binaries: r-devel: MixedDataImpute_0.1.zip, r-release: MixedDataImpute_0.1.zip, r-oldrel: MixedDataImpute_0.1.zip
OS X binaries: r-release: MixedDataImpute_0.1.tgz, r-oldrel: MixedDataImpute_0.1.tgz


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