Package: bbl 1.0.0

Jun Woo

bbl: Boltzmann Bayes Learner

Supervised learning using Boltzmann Bayes model inference, which extends naive Bayes model to include interactions. Enables classification of data into multiple response groups based on a large number of discrete predictors that can take factor values of heterogeneous levels. Either pseudo-likelihood or mean field inference can be used with L2 regularization, cross-validation, and prediction on new data. <doi:10.18637/jss.v101.i05>.

Authors:Jun Woo [aut, cre]

bbl_1.0.0.tar.gz
bbl_1.0.0.tar.gz(r-4.5-noble)bbl_1.0.0.tar.gz(r-4.4-noble)
bbl_1.0.0.tgz(r-4.3-emscripten)
bbl.pdf |bbl.html✨
bbl/json (API)

# Install 'bbl' in R:
install.packages('bbl', repos = 'https://cloud.r-project.org')
Uses libs:
  • gsl– GNU Scientific Library (GSL)
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

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

gslcpp

2.70 score 338 downloads 11 exports 4 dependencies

Last updated 3 years agofrom:d9195ab163. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 20 2025
R-4.5-linux-x86_64OKMar 20 2025
R-4.4-linux-x86_64OKMar 20 2025

Exports:bblbbl.fitcrossValfreq2rawmcSamplemlestimaterandomparrandomsampreadFastaremoveConstsample_xi

Dependencies:plyrpROCRColorBrewerRcpp

bbl: Boltzmann Bayes Learner for High-Dimensional Inference with Discrete Predictors in R

Rendered fromarticle.Rnwusingutils::Sweaveon Mar 20 2025.

Last update: 2021-11-18
Started: 2019-09-04

Citation

To cite bbl in publications use:

Woo J, Wang J (2022). “bbl: Boltzmann Bayes Learner for High-Dimensional Inference with Discrete Predictors in R.” Journal of Statistical Software, 101(5), 1–32. doi:10.18637/jss.v101.i05.

Corresponding BibTeX entry: 

  @Article{,
    title = {{bbl}: {B}oltzmann Bayes Learner for High-Dimensional
      Inference with Discrete Predictors in {R}},
    author = {Jun Woo and Jinhua Wang},
    journal = {Journal of Statistical Software},
    year = {2022},
    volume = {101},
    number = {5},
    pages = {1--32},
    doi = {10.18637/jss.v101.i05},
  }