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outForest  

Multivariate Outlier Detection and Replacement
View on CRAN: Click here


Download and install outForest package within the R console
Install from CRAN:
install.packages("outForest")

Install from Github:
library("remotes")
install_github("cran/outForest")

Install by package version:
library("remotes")
install_version("outForest", "1.0.1")



Attach the package and use:
library("outForest")
Maintained by
Michael Mayer
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-01-13
Latest Update: 2023-05-21
Description:
Provides a random forest based implementation of the method described in Chapter 7.1.2 (Regression model based anomaly detection) of Chandola et al. (2009) . It works as follows: Each numeric variable is regressed onto all other variables by a random forest. If the scaled absolute difference between observed value and out-of-bag prediction of the corresponding random forest is suspiciously large, then a value is considered an outlier. The package offers different options to replace such outliers, e.g. by realistic values found via predictive mean matching. Once the method is trained on a reference data, it can be applied to new data.
How to cite:
Michael Mayer (2020). outForest: Multivariate Outlier Detection and Replacement. R package version 1.0.1, https://cran.r-project.org/web/packages/outForest. Accessed 25 Jun. 2026.
Previous versions and publish date:
0.1.0 (2020-01-13 16:50), 0.1.1 (2021-01-07 03:50), 0.1.2 (2022-01-31 08:40), 0.1.3 (2023-03-30 10:50), 1.0.0 (2023-04-25 18:00)
Other packages that cited outForest R package
View outForest citation profile
Other R packages that outForest depends, imports, suggests or enhances
Complete documentation for outForest
Functions, R codes and Examples using the outForest R package
Some associated functions: Data . generateOutliers . is.outForest . outForest . outliers . plot.outForest . predict.outForest . print.outForest . summary.outForest . 
Some associated R codes: generateOutliers.R . methods.R . outForest.R . predict.R . process_scores.R .  Full outForest package functions and examples
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