missForest: Nonparametric Missing Value Imputation using Random Forest
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 (via 'ranger' or 'randomForest') 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.
| Version: |
1.6.1 |
| Imports: |
randomForest, ranger, foreach, iterators, itertools, doRNG, stats, Rdpack |
| Suggests: |
doParallel, knitr, rmarkdown |
| Published: |
2025-10-26 |
| DOI: |
10.32614/CRAN.package.missForest |
| Author: |
Daniel J. Stekhoven [aut, cre] |
| Maintainer: |
Daniel J. Stekhoven <stekhoven at nexus.ethz.ch> |
| BugReports: |
https://github.com/stekhoven/missForest/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://www.r-project.org, https://github.com/stekhoven/missForest |
| NeedsCompilation: |
no |
| Citation: |
missForest citation info |
| Materials: |
README, NEWS |
| In views: |
MissingData |
| CRAN checks: |
missForest results |
Documentation:
Downloads:
Reverse dependencies:
| Reverse depends: |
bartMachine, imp4p |
| Reverse imports: |
ADAPTS, bartXViz, compIndexBuilder, fastml, funspace, FuzzyImputationTest, GenoPop, highMLR, imanr, KarsTS, longit, MAI, MERO, metamorphr, missCompare, MSPrep, obliqueRSF, pmp, promor, simputation, SmartPhos, speaq, streamDAG |
| Reverse suggests: |
CALIBERrfimpute, DepInfeR, hdImpute, mrIML, MsCoreUtils, mvs, notame, qmtools, tidyLPA |
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