NPBayesImpute: Non-Parametric Bayesian Multiple Imputation for Categorical Data

These routines create multiple imputations of missing at random categorical data, with or without structural zeros. Imputations are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling.

Install the latest version of this package by entering the following in R:
install.packages("NPBayesImpute")
AuthorQuanli Wang, Daniel Manrique-Vallier, Jerome P. Reiter and Jingchen Hu
Date of publication2016-02-09 20:53:48
MaintainerQuanli Wang <quanli@stat.duke.edu>
LicenseGPL (>= 3)
Version0.6

View on CRAN

Files

inst
inst/doc
inst/doc/LCM_Zeros_Imputation_RMaterial.pdf
src
src/CLcm.cpp
src/Makevars
src/CData.h
src/CParam.cpp
src/CParam.h
src/SpecialFunctions.cpp
src/MersenneTwister.h
src/CEnv.cpp
src/CLcm.h
src/lczmain.cpp
src/CTrace.cpp
src/CArrayND.h
src/CEnv.h
src/CData.cpp
src/CTrace.h
src/margin_conditions.h
src/Makevars.win
src/SpecialFunctions.h
src/margin_conditions.cpp
NAMESPACE
demo
demo/example.R
demo/00Index
demo/example_short.R
data
data/NYMockexample.RData
data/NYexample.RData
R
R/ArrayUtils.R R/zzz.R
MD5
DESCRIPTION
man
man/CreateModel.Rd man/GetMCZ.Rd man/MCZ.Rd man/Rcpp_Lcm.Rd man/GetDataFrame.Rd man/Rcpp_Lcm-class.Rd man/Lcm.Rd man/UpdateX.Rd man/X.Rd man/NPBayesImput-package.Rd

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