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arf  

Adversarial Random Forests
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("arf", "0.2.0")



Attach the package and use:
library("arf")
Maintained by
Marvin N. Wright
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-12-16
Latest Update: 2024-01-24
Description:
Adversarial random forests (ARFs) recursively partition data into fully factorized leaves, where features are jointly independent. The procedure is iterative, with alternating rounds of generation and discrimination. Data becomes increasingly realistic at each round, until original and synthetic samples can no longer be reliably distinguished. This is useful for several unsupervised learning tasks, such as density estimation and data synthesis. Methods for both are implemented in this package. ARFs naturally handle unstructured data with mixed continuous and categorical covariates. They inherit many of the benefits of random forests, including speed, flexibility, and solid performance with default parameters. For details, see Watson et al. (2022) .
How to cite:
Marvin N. Wright (2022). arf: Adversarial Random Forests. R package version 0.2.0, https://cran.r-project.org/web/packages/arf. Accessed 22 Dec. 2024.
Previous versions and publish date:
0.1.2 (2022-12-16 17:10), 0.1.3 (2023-02-06 21:22)
Other packages that cited arf R package
View arf citation profile
Other R packages that arf depends, imports, suggests or enhances
Complete documentation for arf
Functions, R codes and Examples using the arf R package
Some associated functions: adversarial_rf . col_rename . expct . forde . forge . leaf_posterior . lik . post_x . prep_evi . prep_x . 
Some associated R codes: adversarial_rf.R . expct.R . forde.R . forge.R . lik.R . utils.R .  Full arf package functions and examples
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