Package: bbl 1.0.0

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]

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

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

Peer review:

Uses libs:
  • gsl– GNU Scientific Library (GSL)
  • c++– GNU Standard C++ Library v3

On CRAN:

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

2.70 score 3 scripts 311 downloads 11 exports 4 dependencies

Last updated 3 years agofrom:d9195ab163. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 08 2024
R-4.5-win-x86_64OKNov 08 2024
R-4.5-linux-x86_64OKNov 08 2024
R-4.4-win-x86_64OKNov 08 2024
R-4.4-mac-x86_64OKNov 08 2024
R-4.4-mac-aarch64OKNov 08 2024
R-4.3-win-x86_64OKNov 08 2024
R-4.3-mac-x86_64OKNov 08 2024
R-4.3-mac-aarch64OKNov 08 2024

Exports:bblbbl.fitcrossValfreq2rawmcSamplemlestimaterandomparrandomsampreadFastaremoveConstsample_xi

Dependencies:plyrpROCRColorBrewerRcpp

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

Rendered fromarticle.Rnwusingutils::Sweaveon Nov 08 2024.

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