The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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 |
| 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 |
| Reference manual: | sbrl.pdf |
| 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 |
Please use the canonical form https://CRAN.R-project.org/package=sbrl to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.