missForest-package: Nonparametric Missing Value Imputation using Random Forest

In missForest: Nonparametric Missing Value Imputation using Random Forest

Nonparametric Missing Value Imputation using Random Forest

Description

'missForest' is used to impute missing values particularly in the case of mixed-type data. It can be used to impute continuous and/or categorical data including complex interactions and nonlinear relations. It yields an out-of-bag (OOB) imputation error estimate. Moreover, it can be run parallel to save computation time.

Details

Package:	missForest
Type:	Package
Version:	1.4
Date:	2013-12-31
License:	GPL (>= 2)
LazyLoad:	yes

The main function of the package is missForest implementing the nonparametric missing value imputation. See missForest for more details.

Author(s)

Daniel J. Stekhoven, [e-mail]

References

Stekhoven, D.J. and Buehlmann, P. (2012), 'MissForest - nonparametric missing value imputation for mixed-type data', Bioinformatics, 28(1) 2012, 112-118, doi: 10.1093/bioinformatics/btr597

missForest documentation built on April 14, 2022, 5:05 p.m.