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missForestPredict  

Missing Value Imputation using Random Forest for Prediction Settings
View on CRAN: Click here


Download and install missForestPredict package within the R console
Install from CRAN:
install.packages("missForestPredict")

Install from Github:
library("remotes")
install_github("cran/missForestPredict")

Install by package version:
library("remotes")
install_version("missForestPredict", "1.0.1")



Attach the package and use:
library("missForestPredict")
Maintained by
Elena Albu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-12-12
Latest Update: 2025-05-24
Description:
Missing data imputation based on the 'missForest' algorithm (Stekhoven, Daniel J (2012) ) with adaptations for prediction settings. The function missForest() is used to impute a (training) dataset with missing values and to learn imputation models that can be later used for imputing new observations. The function missForestPredict() is used to impute one or multiple new observations (test set) using the models learned on the training data.
How to cite:
Elena Albu (2023). missForestPredict: Missing Value Imputation using Random Forest for Prediction Settings. R package version 1.0.1, https://cran.r-project.org/web/packages/missForestPredict. Accessed 05 Mar. 2026.
Previous versions and publish date:
1.0 (2023-12-12 19:20)
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Complete documentation for missForestPredict
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