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 (ranger by default)

Description

The missForest package provides nonparametric missing-value imputation for mixed-type data (continuous and categorical). It models each variable with missingness using random forests that learn complex interactions and nonlinear relations and returns out-of-bag (OOB) error estimates. The default backend is ranger for speed and scalability, with an optional legacy randomForest backend for backward compatibility. Parallelization is supported either across variables (via foreach/doRNG) or within forests (via ranger threads).

Details

Package:	missForest
Type:	Package
Version:	1.6
Date:	2025-10-13
License:	GPL (>= 2)

The main function is missForest, which iteratively imputes missing entries by fitting per-variable random forests to the currently imputed data matrix. The implementation now defaults to a ranger-based backend while preserving the original randomForest-based behavior via the backend argument. See missForest for arguments, details on stopping criteria, OOB error reporting (NRMSE for numeric variables and PFC for factors), and parallel options.

Author(s)

Daniel J. Stekhoven [aut, cre]

References

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StekhovenBuehlmann2012missForest

See Also

missForest, mixError, prodNA

missForest documentation built on Nov. 5, 2025, 6 p.m.