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MixedDataImpute (version 0.1)

Missing Data Imputation for Continuous and Categorical Data using Nonparametric Bayesian Joint Models

Description

Missing data imputation for continuous and categorical data, using nonparametric Bayesian joint models (specifically the hierarchically coupled mixture model with local dependence described in Murray and Reiter (2015); see 'citation("MixedDataImpute")' or http://arxiv.org/abs/1410.0438). See '?hcmm_impute' for example usage.

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Install

install.packages('MixedDataImpute')

Monthly Downloads

3

Version

0.1

License

GPL-3

Maintainer

Jared S Murray

Last Published

February 7th, 2016

Functions in MixedDataImpute (0.1)

remap_imputations

Map raw imputations back to original scale/factor labels.
hcmm_hyperpar

Generate a list of hyperparameters
prepare_data

Prepare a dataset for imputation
sipp08

Housholder earnings from the SIPP
hcmm_impute

Generate multiply imputed datasets
MixedDataImpute

Missing data imputation for continuous and categorical data using nonparametric Bayesian joint models