# Non-Parametric Bayesian Multiple Imputation for Categorical Data

## Documentation for package ‘NPBayesImputeCat’ version 0.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Code demos. Use demo() to run them.

## Help Pages

NPBayesImputeCat-package | Bayesian Multiple Imputation for Large-Scale Categorical Data with Structural Zeros
CreateModel | Create and initialize the Rcpp_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.
Lcm | RCPP implemenation of the library
MCZ | Example dataframe for structrual zeros.
NPBayesImputeCat | Bayesian Multiple Imputation for Large-Scale Categorical Data with Structural Zeros
Rcpp_Lcm | RCPP implemenation of the library
Rcpp_Lcm-class | Class '"Rcpp_Lcm"'
ss16pusa_ds_MCZ | Example dataframe for structrual zeros.
ss16pusa_mi_MCZ | Example dataframe for structrual zeros.
ss16pusa_sample | Example dataframe for input categorical data.
ss16pusa_sample_nozeros | Example dataframe for input categorical data.
ss16pusa_sample_nozeros_miss | Example dataframe for input categorical data with missing values.
ss16pusa_sample_zeros | Example dataframe for input categorical data.
ss16pusa_sample_zeros_miss | Example dataframe for input categorical data with missing values.
UpdateX | Allow user to update the model with data matrix of same kind.
X | Example dataframe for input categorical data with missing values.