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NPBayesImputeCat (version 0.2)

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

217

Version

0.2

License

GPL (>= 3)

Maintainer

Jingchen Hu

Last Published

November 8th, 2019

Functions in NPBayesImputeCat (0.2)

ss16pusa_sample

Example dataframe for input categorical data.
ss16pusa_sample_nozeros

Example dataframe for input categorical data.
ss16pusa_mi_MCZ

Example dataframe for structrual zeros.
ss16pusa_ds_MCZ

Example dataframe for structrual zeros.
Lcm

RCPP implemenation of the library
ss16pusa_sample_zeros

Example dataframe for input categorical data.
ss16pusa_sample_nozeros_miss

Example dataframe for input categorical data with missing values.
ss16pusa_sample_zeros_miss

Example dataframe for input categorical data with missing values.
GetMCZ

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

Create and initialize the Rcpp_Lcm model object
Rcpp_Lcm

RCPP implemenation of the library
Rcpp_Lcm-class

Class "Rcpp_Lcm"
MCZ

Example dataframe for structrual zeros.
X

Example dataframe for input categorical data with missing values.
UpdateX

Allow user to update the model with data matrix of same kind.
NPBayesImputeCat-package

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

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