BMLCimpute

Bayesian Multilevel Latent Class Models for the Multiple Imputation of Nested Categorical Data

An R package for the multiple imputation of single-level and nested categorical data by means of Bayesian Multilevel Latent Class models.

Author

Davide Vidotto [email protected]

Description

BMLCimpute allows researchers and users of categorical datasets with missing data to perform Multiple Imputation via Bayesian latent class models. Data can be either single- or multi-level. Model estimation and imputations are implemented via a Gibbs sampler run with the Rcpp package interface. The function multilevelLCMI performs the imputations. Prior to the imputation step, data should be processed with the function convData; the resulting list is then passed as input to the multilevelLCMI. Complete datasets are obtained via the compData function. Check package documentation in inst/doc for further information.

Functions

• multilevelLCMI for the imputations and model estimation (internally calls Rcpp code)
• convData for data preparation (preprocessing)
• compData for dataset completion

Install

devtools::install_github("davidevdt/BMLCimpute")

Version

0.0.1

Depends

R (>= 3.3.3)

License

GPL-2