Non-Parametric Bayesian Multiple Imputation for Categorical Data

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Documentation for package ‘NPBayesImputeCat’ version 0.2

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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.