Package: missForest 1.5

Daniel J. Stekhoven

missForest: 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.

Authors:Daniel J. Stekhoven <[email protected]>

missForest_1.5.tar.gz
missForest_1.5.zip(r-4.5)missForest_1.5.zip(r-4.4)missForest_1.5.zip(r-4.3)
missForest_1.5.tgz(r-4.4-any)missForest_1.5.tgz(r-4.3-any)
missForest_1.5.tar.gz(r-4.5-noble)missForest_1.5.tar.gz(r-4.4-noble)
missForest_1.5.tgz(r-4.4-emscripten)missForest_1.5.tgz(r-4.3-emscripten)
missForest.pdf |missForest.html✨
missForest/json (API)

Peer review:

Bug tracker:https://github.com/stekhoven/missforest/issues

On CRAN:

5 exports 88 stars 7.27 score 8 dependencies 31 dependents 178 mentions 1.0k scripts 7.0k downloads

Last updated 12 months agofrom:5e5e26035e. Checks:OK: 5 NOTE: 2. Indexed: yes.

Exports:missForestmixErrornrmseprodNAvarClass

Dependencies:codetoolsdigestdoRNGforeachiteratorsitertoolsrandomForestrngtools