sbrl: Scalable Bayesian Rule Lists Model

An efficient implementation of Scalable Bayesian Rule Lists Algorithm, a competitor algorithm for decision tree algorithms; see Hongyu Yang, Cynthia Rudin, Margo Seltzer (2017) <https://proceedings.mlr.press/v70/yang17h.html>. It builds from pre-mined association rules and have a logical structure identical to a decision list or one-sided decision tree. Fully optimized over rule lists, this algorithm strikes practical balance between accuracy, interpretability, and computational speed.

Version:	1.4
Imports:	Rcpp (≥ 0.12.4), arules, methods
LinkingTo:	Rcpp
Published:	2024-04-08
DOI:	10.32614/CRAN.package.sbrl
Author:	Hongyu Yang [aut, cre], Morris Chen [ctb], Cynthia Rudin [aut, ctb], Margo Seltzer [aut, ctb], The President and Fellows of Harvard College [cph]
Maintainer:	Hongyu Yang <edwardyhy1 at gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
SystemRequirements:	gmp (>= 4.2.0), gsl
CRAN checks:	sbrl results

Documentation:

Reference manual:

sbrl.html , sbrl.pdf

Downloads:

Package source:	sbrl_1.4.tar.gz
Windows binaries:	r-devel: sbrl_1.4.zip, r-release: sbrl_1.4.zip, r-oldrel: sbrl_1.4.zip
macOS binaries:	r-release (arm64): sbrl_1.4.tgz, r-oldrel (arm64): sbrl_1.4.tgz, r-release (x86_64): sbrl_1.4.tgz, r-oldrel (x86_64): sbrl_1.4.tgz
Old sources:	sbrl archive

Reverse dependencies:

Reverse suggests:

qCBA

Linking:

Please use the canonical form https://CRAN.R-project.org/package=sbrl to link to this page.