# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "bayesRecon" in publications use:' type: software license: LGPL-3.0-or-later title: 'bayesRecon: Probabilistic Reconciliation via Conditioning' version: 0.3.2 doi: 10.32614/CRAN.package.bayesRecon abstract: Provides methods for probabilistic reconciliation of hierarchical forecasts of time series. The available methods include analytical Gaussian reconciliation (Corani et al., 2021) , MCMC reconciliation of count time series (Corani et al., 2024) , Bottom-Up Importance Sampling (Zambon et al., 2024) , methods for the reconciliation of mixed hierarchies (Mix-Cond and TD-cond) (Zambon et al., 2024. The 40th Conference on Uncertainty in Artificial Intelligence, accepted). authors: - family-names: Azzimonti given-names: Dario email: dario.azzimonti@gmail.com orcid: https://orcid.org/0000-0001-5080-3061 - family-names: Rubattu given-names: Nicolò email: nicolo.rubattu@idsia.ch orcid: https://orcid.org/0000-0002-2703-1005 - family-names: Zambon given-names: Lorenzo email: lorenzo.zambon@idsia.ch orcid: https://orcid.org/0000-0002-8939-993X - family-names: Corani given-names: Giorgio email: giorgio.corani@idsia.ch orcid: https://orcid.org/0000-0002-1541-8384 repository: https://idsia.r-universe.dev repository-code: https://github.com/IDSIA/bayesRecon commit: d327985b660a227e233ee6c4fae0cd498fcafe2b url: https://github.com/IDSIA/bayesRecon date-released: '2024-11-04' contact: - family-names: Azzimonti given-names: Dario email: dario.azzimonti@gmail.com orcid: https://orcid.org/0000-0001-5080-3061