CreateModel             Create and initialize the 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
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                     Class '"Rcpp_Lcm"'
MCZ                     Example dataframe for structrual zeros based on
                        the NYMockexample dataset.
NPBayesImputeCat-package
                        Bayesian Multiple Imputation for Large-Scale
                        Categorical Data with Structural Zeros
Rcpp_Lcm-class          Rcpp implemenation of the Lcm functions
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.
compute_probs           Estimating marginal and joint probabilities in
                        imputed or synthetic datasets
fit_GLMs                Fit GLM models for imputed or synthetic
                        datasets
kstar_MCMCdiag          Perform MCMC diagnostics for kstar
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
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
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).
