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 Product-Multinomials (DPMCPM, Wang et al. (2017)
|Date of publication||2017-12-07 15:05:43 UTC|
|Maintainer||Chaojie Wang <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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