missForest: Nonparametric missing value imputation using random forest
Abstract: missForest imputes 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 and can be run in parallel to save computation time. missForest has been used to, among other things, impute variable star colors in an All-Sky Automated Survey (ASAS) dataset of variable stars with no NOMAD match.
Credit: Stekhoven, Daniel J.
Site: https://github.com/stekhoven/missForest
https://doi.org/10.1093/bioinformatics/btr597
Bibcode: 2015ascl.soft05011S
Preferred citation method: Please see citation information here: https://cran.r-project.org/web/packages ... ation.html
ID: ascl:1505.011