# Non-Parametric Bayesian Multiple Imputation for Categorical Data

## Documentation for package ‘NPBayesImputeCat’ version 0.3

- 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
compute_probs | Estimating marginal and joint probabilities in imputed or synthetic datasets
CreateModel | Create and initialize the Rcpp_Lcm model object
DPMPM_nozeros_imp | Use DPMPM models to impute missing data where there are no structural zeros
DPMPM_nozeros_syn | Use DPMPM models to synthesize data where there are no structural zeros
DPMPM_zeros_imp | Use DPMPM models to impute missing data where there are no structural zeros
fit_GLMs | Fit GLM models for imputed or synthetic datasets
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
marginal_compare_all_imp | Plot estimated marginal probabilities from observed data vs imputed datasets
marginal_compare_all_syn | Plot estimated marginal probabilities from observed data vs synthetic datasets
MCZ | Example dataframe for structrual zeros based on the NYMockexample dataset.
NPBayesImputeCat | Bayesian Multiple Imputation for Large-Scale Categorical Data with Structural Zeros
pool_estimated_probs | Pool probability estimates from imputed or synthetic datasets
pool_fitted_GLMs | Pool estimates of fitted GLM models in imputed or synthetic datasets
Rcpp_Lcm | RCPP implemenation of the library
Rcpp_Lcm-class | Class '"Rcpp_Lcm"'
ss16pusa_ds_MCZ | Example dataframe for structrual zeros based on the ss16pusa_sample_zeros dataset.
ss16pusa_mi_MCZ | Example dataframe for structrual zeros based on the ss16pusa_sample_zeros dataset.
ss16pusa_sample_nozeros | Example dataframe for input categorical data without structural zeros (without missing values).
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).
ss16pusa_sample_zeros_miss | Example dataframe for input categorical data with structural zeros (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 based on the NYMockexample dataset.