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missForest  

Nonparametric Missing Value Imputation using Random Forest
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", "1.5")



Attach the package and use:
library("missForest")
Maintained by
Daniel J. Stekhoven
[Scholar Profile | Author Map]
First Published: 2011-05-13
Latest Update: 2022-04-14
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 1.5, https://cran.r-project.org/web/packages/missForest. Accessed 14 May. 2025.
Previous versions and publish date:
1.0 (2011-05-13 16:42), 1.1 (2011-11-04 20:14), 1.2 (2012-02-20 16:08), 1.3-1 (2013-12-10 15:48), 1.3 (2012-06-26 12:52), 1.4 (2013-12-31 16:17)
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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