Package: sbrl 1.4

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.

Authors:Hongyu Yang [aut, cre], Morris Chen [ctb], Cynthia Rudin [aut, ctb], Margo Seltzer [aut, ctb], The President and Fellows of Harvard College [cph]

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sbrl.pdf |sbrl.html
sbrl/json (API)

# Install 'sbrl' in R:
install.packages('sbrl', repos = c('https://hongyuy.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • gsl– GNU Scientific Library (GSL)
  • gmp– Multiprecision arithmetic library
  • c++– GNU Standard C++ Library v3
Datasets:
  • tictactoe - SHUFFLED TIC-TAC-TOE-ENDGAME DATASET

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

5 exports 4 stars 0.71 score 5 dependencies 16 scripts 184 downloads

Last updated 5 months agofrom:76a0e9f5ee. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 27 2024
R-4.5-win-x86_64OKAug 27 2024
R-4.5-linux-x86_64OKAug 27 2024
R-4.4-win-x86_64OKAug 27 2024
R-4.4-mac-x86_64OKAug 27 2024
R-4.4-mac-aarch64OKAug 27 2024
R-4.3-win-x86_64OKAug 27 2024
R-4.3-mac-x86_64OKAug 27 2024
R-4.3-mac-aarch64OKAug 27 2024

Exports:get_data_feature_matpredict.sbrlprint.sbrlsbrlshow.sbrl

Dependencies:arulesgenericslatticeMatrixRcpp