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# 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 "bacontrees" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'bacontrees: Bayesian Context Trees for Discrete Sequence Data'
version: 1.0.0
abstract: Models discrete sequential data using Bayesian Context Trees. Context trees,
  also known as Variable Length Markov Chains (VLMCs), are parsimonious Markov models
  where the order of dependence can vary with the observed past. Provides a generic
  'R6' class structure that exposes the full tree for building custom algorithms,
  exact Bayesian inference via a bottom-up recursive algorithm (closed-form marginal
  likelihood, Maximum A Posteriori (MAP) tree, exact posterior probabilities, and
  exact sampling from the posterior), a frequentist estimator via the context algorithm
  with likelihood-ratio pruning, simulation utilities, and a Metropolis-Hastings sampler.
  See Paulichen and Freguglia (2026) <https://doi.org/10.48550/arXiv.2603.25806>.
authors:
- family-names: Freguglia
  given-names: Victor
  email: victorfreguglia@gmail.com
  orcid: https://orcid.org/0000-0002-6189-4453
repository: https://cran.r-universe.dev
commit: 48a8c0f263b966b11796735a755bbc41705813cc
date-released: '2026-05-12'
contact:
- family-names: Freguglia
  given-names: Victor
  email: victorfreguglia@gmail.com
  orcid: https://orcid.org/0000-0002-6189-4453