missForest
1.6.1Nonparametric Missing Value Imputation using Random Forest
Overview
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 (via 'ranger' or 'randomForest') 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.
Install
Health
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
Documentation signals are not tracked yet for this package.
Downloads
Repository
Repository practices
3 development-tooling and community-health practices detected across 2 families in the upstream repository
Checks run against github.com/stekhoven/missforest on 2026-07-19.
Dependencies
Code & Tests
People & History
8 releases. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.6.1Latest
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- 1.52022-04-14 · diff ↗
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- RR 3.3.0 released · 2016-05-03
- RR 3.2.0 released · 2015-04-16
- RR 3.1.0 released · 2014-04-10
- 1.42013-12-31 · diff ↗
Package metadata
- First published
- 2011-05-13
- Total releases
- 8 / 15 yrs
- License
- GPL (>= 2) OSI
- Download size
- 263 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet