Rcpp_Lcm

<p><a rd-options="" href="/link/Rcpp_Lcm-class?package=NPBayesImputeCat&version=0.2" data-mini-rdoc="NPBayesImputeCat::Rcpp_Lcm-class">Rcpp_Lcm-class</a></p>

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) <doi:10.1080/10618600.2013.844700>.

Jingchen Hu

NPBayesImputeCat

Non-Parametric Bayesian Multiple Imputation for Categorical Data

Rcpp_Lcm function

<p><a rd-options='' href='Rcpp_Lcm-class'>Rcpp_Lcm-class</a></p>

Rcpp_Lcm: RCPP implemenation of the library

Description

Arguments