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

Package: BaBooN
Version: 0.2-0
Date: 2015-06-15
Title: Bayesian Bootstrap Predictive Mean Matching - Multiple and
Single Imputation for Discrete Data
Author: Florian Meinfelder [aut, cre],
Thorsten Schnapp [aut]
Maintainer: Florian Meinfelder <florian.meinfelder@uni-bamberg.de>
Description: 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.
License: GPL (>= 2)
Depends: R (>= 3.1.0), Rcpp (>= 0.11.2)
Imports: Hmisc, MASS, nnet, coda
Suggests: mice, lattice, norm
LinkingTo: Rcpp, RcppArmadillo
ByteCompile: true
URL: http://www.r-project.org
Packaged: 2015-06-15 13:47:36 UTC; ThS
Repository: CRAN
Date/Publication: 2015-06-15 17:30:31
NeedsCompilation: yes