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NPBayesImputeCat (version 0.6)

Non-Parametric Bayesian Multiple Imputation for Categorical Data

Description

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) .

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Version

Install

install.packages('NPBayesImputeCat')

Monthly Downloads

271

Version

0.6

License

GPL (>= 3)

Maintainer

Jingchen Hu

Last Published

December 1st, 2025

Functions in NPBayesImputeCat (0.6)

compute_probs

Estimating marginal and joint probabilities in imputed or synthetic datasets
ss16pusa_sample_nozeros

Example dataframe for input categorical data without structural zeros (without missing values).
ss16pusa_mi_MCZ

Example dataframe for structrual zeros based on the ss16pusa_sample_zeros dataset.
kstar_MCMCdiag

Perform MCMC diagnostics for kstar
ss16pusa_sample_nozeros_miss

Example dataframe for input categorical data without structural zeros (with missing values).
ss16pusa_sample_zeros

Example dataframe for input categorical data with structural zeros (without missing values).
marginal_compare_all_imp

Plot estimated marginal probabilities from observed data vs imputed datasets
ss16pusa_sample_zeros_miss

Example dataframe for input categorical data with structural zeros (with missing values).
fit_GLMs

Fit GLM models for imputed or synthetic datasets
Rcpp_Lcm-class

Rcpp implemenation of the Lcm functions
NPBayesImputeCat-package

Bayesian Multiple Imputation for Large-Scale Categorical Data with Structural Zeros
CreateModel

Create and initialize the Lcm model object
GetDataFrame

Convert imputed data to a dataframe, using the same setting from original input data.
GetMCZ

Convert disjointed structrual zeros to a dataframe, using the same setting from original structrual zero data.
DPMPM_zeros_imp

Use DPMPM models to impute missing data where there are no structural zeros
MCZ

Example dataframe for structrual zeros based on the NYMockexample dataset.
DPMPM_nozeros_syn

Use DPMPM models to synthesize data where there are no structural zeros
DPMPM_nozeros_imp

Use DPMPM models to impute missing data where there are no structural zeros
pool_fitted_GLMs

Pool estimates of fitted GLM models in imputed or synthetic datasets
Lcm

Class "Rcpp_Lcm"
ss16pusa_ds_MCZ

Example dataframe for structrual zeros based on the ss16pusa_sample_zeros dataset.
pool_estimated_probs

Pool probability estimates from imputed or synthetic datasets
marginal_compare_all_syn

Plot estimated marginal probabilities from observed data vs synthetic datasets
UpdateX

Allow user to update the model with data matrix of same kind.
X

Example dataframe for input categorical data with missing values based on the NYMockexample dataset.