CRAN/E | missForest

missForest

Nonparametric Missing Value Imputation using Random Forest

Installation

About

The function 'missForest' in this package is used to impute missing values particularly in the case of mixed-type data. It uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation. It can be run in parallel to save computation time.

Citation missForest citation info
www.r-project.org
github.com/stekhoven/missForest

Key Metrics

Version 1.5
Published 2022-04-14 740 days ago
Needs compilation? no
License GPL-2
License GPL-3
CRAN checks missForest results

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Maintainer

Maintainer

Daniel J. Stekhoven

stekhoven@stat.math.ethz.ch

Authors

Daniel J. Stekhoven

Material

README
Reference manual
Package source

In Views

MissingData

Vignettes

missForest_1.5

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

missForest archive

Imports

randomForest
foreach
itertools
iterators
doRNG

Suggests

doParallel

Reverse Depends

bartMachine
imp4p

Reverse Imports

ADAPTS
autohd
highMLR
KarsTS
longit
MAI
MERO
missCompare
MSPrep
NADIA
obliqueRSF
pmp
proFIA
promor
simputation
speaq

Reverse Suggests

CALIBERrfimpute
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