NPBayesImputeCat
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) doi:10.1080/10618600.2013.844700.
- Version0.5
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- Last release10/03/2022
Documentation
Team
Jingchen Hu
Daniel Manrique-Vallier
Quanli Wang
Jerome P. Reiter
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- Depends1 package
- Imports5 packages
- Linking To1 package
- Reverse Imports1 package