NPBayesImputeCat (0.2)

Non-Parametric Bayesian Multiple Imputation for Categorical Data.

These routines create multiple imputations of missing at random categorical data, and create multiply imputed synthesis of categorical data, with or without structural zeros. Imputations and syntheses are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling, described in Manrique-Vallier and Reiter (2014) .

Maintainer: Jingchen Hu
Author(s): Quanli Wang, Daniel Manrique-Vallier, Jerome P. Reiter and Jingchen Hu

License: GPL (>= 3)

Uses: Rcpp

Released 4 days ago.