missRanger

Fast Imputation of Missing Values

Alternative implementation of the beautiful 'MissForest' algorithm used to impute mixed-type data sets by chaining tree ensembles, introduced by Stekhoven, D.J. and Buehlmann, P. (2012) <doi:10.1093/bioinformatics/btr597>. Under the hood, it uses the lightning fast random jungle package 'ranger'. Between the iterative model fitting, we offer the option of using predictive mean matching. This firstly avoids imputation with values not already present in the original data (like a value 0.3334 in 0-1 coded variable). Secondly, predictive mean matching tries to raise the variance in the resulting conditional distributions to a realistic level. This would allow e.g. to do multiple imputation when repeating the call to missRanger().

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Description file content

Package
missRanger
Title
Fast Imputation of Missing Values
Version
1.0.3
Description
Alternative implementation of the beautiful 'MissForest' algorithm used to impute mixed-type data sets by chaining tree ensembles, introduced by Stekhoven, D.J. and Buehlmann, P. (2012) . Under the hood, it uses the lightning fast random jungle package 'ranger'. Between the iterative model fitting, we offer the option of using predictive mean matching. This firstly avoids imputation with values not already present in the original data (like a value 0.3334 in 0-1 coded variable). Secondly, predictive mean matching tries to raise the variance in the resulting conditional distributions to a realistic level. This would allow e.g. to do multiple imputation when repeating the call to missRanger().
Depends
R (>= 3.4.0)
License
GPL (>= 2)
Encoding
UTF-8
LazyData
true
Type
Package
Date
2018-05-15
Imports
stats, FNN (>= 1.1), ranger (>= 0.8.0)
Author
Michael Mayer [aut, cre, cph]
Maintainer
Michael Mayer
RoxygenNote
6.0.1
NeedsCompilation
no
Packaged
2018-05-15 18:26:52 UTC; Michael
Repository
CRAN
Date/Publication
2018-05-15 18:40:10 UTC

install.packages('missRanger')

1.0.3

4 days ago

Michael Mayer

GPL (>= 2)

Depends on

R (>= 3.4.0)

Imports

stats, FNN (>= 1.1), ranger (>= 0.8.0)

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