NPBayesImpute: Non-parametric Bayesian Multiple Imputation for Categorical Data
These routines create multiple imputations of missing at random categorical data, with or without structural zeros. Imputations are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling.
Version: |
0.3 |
Depends: |
methods, Rcpp (≥ 0.10.2) |
LinkingTo: |
Rcpp |
Published: |
2014-09-30 |
Author: |
Quanli Wang, Daniel Manrique-Vallier, Jerome P. Reiter and Jingchen Hu |
Maintainer: |
Quanli Wang <quanli at stat.duke.edu> |
License: |
GPL (≥ 3) |
NeedsCompilation: |
yes |
CRAN checks: |
NPBayesImpute results |
Downloads: