# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "bbl" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'bbl: Boltzmann Bayes Learner'
version: 1.0.0
doi: 10.18637/jss.v101.i05
identifiers:
- type: doi
  value: 10.32614/CRAN.package.bbl
abstract: 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.
  <https://doi.org/10.18637/jss.v101.i05>.
authors:
- family-names: Woo
  given-names: Jun
  email: junwoo035@gmail.com
  orcid: https://orcid.org/0000-0003-3220-2064
preferred-citation:
  type: article
  title: 'bbl: Boltzmann Bayes Learner for High-Dimensional Inference with Discrete
    Predictors in R'
  authors:
  - family-names: Woo
    given-names: Jun
    email: junwoo035@gmail.com
    orcid: https://orcid.org/0000-0003-3220-2064
  - family-names: Wang
    given-names: Jinhua
  journal: Journal of Statistical Software
  year: '2022'
  volume: '101'
  issue: '5'
  doi: 10.18637/jss.v101.i05
  start: '1'
  end: '32'
repository: https://hjunwoo.r-universe.dev
commit: d9195ab16399825d290191908023e1517af87631
date-released: '2022-01-27'
contact:
- family-names: Woo
  given-names: Jun
  email: junwoo035@gmail.com
  orcid: https://orcid.org/0000-0003-3220-2064