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ranktreeEnsemble  

Ensemble Models of Rank-Based Trees with Extracted Decision Rules
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("ranktreeEnsemble", "0.23")



Attach the package and use:
library("ranktreeEnsemble")
Maintained by
Min Lu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-08-08
Latest Update: 2023-08-18
Description:
Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Decision rules can be extracted from trees.
How to cite:
Min Lu (2023). ranktreeEnsemble: Ensemble Models of Rank-Based Trees with Extracted Decision Rules. R package version 0.23, https://cran.r-project.org/web/packages/ranktreeEnsemble. Accessed 21 Nov. 2024.
Previous versions and publish date:
0.21 (2023-08-08 18:20), 0.22 (2023-08-18 22:20)
Other packages that cited ranktreeEnsemble R package
View ranktreeEnsemble citation profile
Other R packages that ranktreeEnsemble depends, imports, suggests or enhances
Complete documentation for ranktreeEnsemble
Functions, R codes and Examples using the ranktreeEnsemble R package
Some associated functions: extract.rules . importance . pair . predict . ranktreeEnsemble-package . rboost . rforest . select.rules . tnbc . 
Some associated R codes: RcppExports.R . hidden.R . importance.R . pair.R . predict.R . rboost.R . rforest.R . rforest.tree.R . rules.R .  Full ranktreeEnsemble package functions and examples
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