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BaBooN (version 0.2-0)

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

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.

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Version

Install

install.packages('BaBooN')

Version

0.2-0

License

GPL (>= 2)

Last Published

June 15th, 2015

Functions in BaBooN (0.2-0)

summary.imp

Summary method for objects of class ‘imp’
MI.inference

Multiple Imputation inference
impdiagnosticconversion

Conversion from BBPMM output to mice's mids object or prepares imputed data for coda's mcmc or mcmc.list objects
rowimpPrep

Missing-data pattern identifier
BBPMM.row

(Multiple) Imputation of variable vectors
BBPMM

(Multiple) Imputation through Bayesian Bootstrap Predictive Mean Matching (BBPMM)
summary.impprep

Summary method for objects of class ‘impprep’
dmi

Data monotonicity index for missing values
BaBooN-package

Package for multiple imputation of missing values based on Bayesian Bootstrap with Predictive Mean Matching.