Missingness in categorical data is a common problem in various real applications.
Traditional approaches either utilize only the complete observations or impute the missing data by some ad hoc methods rather than the true conditional distribution of the missing data,
thus losing or distorting the rich information in the partial observations.
This package develops a Bayesian nonparametric approach, the Dirichlet Process Mixture of Collapsed ProductMultinomials (DPMCPM, Wang et al. (2017)
Package details 


Author  Chaojie Wang 
Date of publication  20171207 15:05:43 UTC 
Maintainer  Chaojie Wang <[email protected]> 
License  GPL (>= 2) 
Version  1.4.0 
Package repository  View on CRAN 
Installation 
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