BaBooN: Bayesian Bootstrap Predictive Mean Matching - Multiple and Single Imputation for Discrete Data
Version 0.2-0

Included are two variants of Bayesian Bootstrap Predictive Mean Matching to multiply impute missing data. The first variant is a variable-by-variable imputation combining sequential regression and Predictive Mean Matching (PMM) that has been extended for unordered categorical data. The Bayesian Bootstrap allows for generating approximately proper multiple imputations. The second variant is also based on PMM, but the focus is on imputing several variables at the same time. The suggestion is to use this variant, if the missing-data pattern resembles a data fusion situation, or any other missing-by-design pattern, where several variables have identical missing-data patterns. Both variants can be run as 'single imputation' versions, in case the analysis objective is of a purely descriptive nature.

Package details

AuthorFlorian Meinfelder [aut, cre], Thorsten Schnapp [aut]
Date of publication2015-06-15 17:30:31
MaintainerFlorian Meinfelder <[email protected]>
LicenseGPL (>= 2)
Version0.2-0
URL http://www.r-project.org
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("BaBooN")

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BaBooN documentation built on May 30, 2017, 12:30 a.m.