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missForest
View on CRAN: Click
here
Download and install missForest package within the R console
Install from CRAN:
install.packages("missForest")
Install from Github:
library("remotes")
install_github("cran/missForest") Install by package version:
library("remotes")
install_version("missForest", "1.6.1") Attach the package and use:
library("missForest")
Maintained by
Daniel J. Stekhoven
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2011-05-13
Latest Update: 2025-09-01
Description:
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.
How to cite:
Daniel J. Stekhoven (2011). missForest: Nonparametric Missing Value Imputation using Random Forest. R package version 1.6.1, https://cran.r-project.org/web/packages/missForest. Accessed 29 Sep. 2026.
Previous versions and publish date:
Other packages that cited missForest R package
View missForest citation profile
Other R packages that missForest depends,
imports, suggests or enhances
Complete documentation for missForest
Functions, R codes and Examples using
the missForest R package
Some associated functions: missForest-package . missForest . mixError . nrmse . prodNA . varClass .
Some associated R codes: missForest.R . mixError.R . nrmse.R . prodNA.R . varClass.R . Full missForest package functions and examples
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