missForest (1.4)

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Nonparametric Missing Value Imputation using Random Forest.


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.

Maintainer: Daniel J. Stekhoven,
Author(s): Daniel J. Stekhoven <stekhoven@stat.math.ethz.ch>

License: GPL (>= 2)

Uses: foreach, itertools, randomForest
Reverse depends: bartMachine
Reverse suggests: CALIBERrfimpute, CBDA, NNLM, simputation, tidyLPA

Released over 6 years ago.

5 previous versions



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