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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>.
Version: | 1.0.0 |
Depends: | R (≥ 3.6.0) |
Imports: | methods, stats, utils, Rcpp (≥ 0.12.16), pROC, RColorBrewer |
LinkingTo: | Rcpp |
Suggests: | glmnet, BiocManager, Biostrings |
Published: | 2022-01-27 |
DOI: | 10.32614/CRAN.package.bbl |
Author: | Jun Woo |
Maintainer: | Jun Woo <junwoo035 at gmail.com> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: | yes |
Citation: | bbl citation info |
Materials: | README |
CRAN checks: | bbl results |
Reference manual: | bbl.pdf |
Vignettes: |
bbl: Boltzmann Bayes Learner for High-Dimensional Inference with Discrete Predictors in R |
Package source: | bbl_1.0.0.tar.gz |
Windows binaries: | r-devel: bbl_1.0.0.zip, r-release: bbl_1.0.0.zip, r-oldrel: bbl_1.0.0.zip |
macOS binaries: | r-release (arm64): bbl_1.0.0.tgz, r-oldrel (arm64): bbl_1.0.0.tgz, r-release (x86_64): bbl_1.0.0.tgz, r-oldrel (x86_64): bbl_1.0.0.tgz |
Old sources: | bbl archive |
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These binaries (installable software) and packages are in development.
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