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.
Documentation:
Downloads:
Reverse dependencies:
| Reverse depends: |
bartMachine, imp4p |
| Reverse imports: |
ADAPTS, funspace, FuzzyImputationTest, GenoPop, highMLR, imanr, KarsTS, longit, MAI, MERO, missCompare, MSPrep, obliqueRSF, pmp, promor, simputation, speaq |
| Reverse suggests: |
CALIBERrfimpute, DepInfeR, hdImpute, MsCoreUtils, mvs, qmtools, tidyLPA |
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