bayesRecon: Probabilistic Reconciliation via Conditioning

Provides methods for probabilistic reconciliation of hierarchical forecasts of time series. The available methods include analytical Gaussian reconciliation (Corani et al., 2021) <doi:10.1007/978-3-030-67664-3_13>, MCMC reconciliation of count time series (Corani et al., 2022) <doi:10.48550/arXiv.2207.09322>, Bottom-Up Importance Sampling (Zambon et al., 2022) <doi:10.48550/arXiv.2210.02286>.

Version: 0.2.0
Depends: R (≥ 4.1.0)
Imports: stats, utils, lpSolve (≥ 5.6.18)
Suggests: knitr, rmarkdown, forecast, glarma, scoringRules, testthat (≥ 3.0.0)
Published: 2023-12-19
Author: Dario Azzimonti ORCID iD [aut, cre], Nicolò Rubattu ORCID iD [aut], Lorenzo Zambon ORCID iD [aut], Giorgio Corani ORCID iD [aut]
Maintainer: Dario Azzimonti <dario.azzimonti at>
License: LGPL (≥ 3)
NeedsCompilation: no
Materials: README NEWS
In views: TimeSeries
CRAN checks: bayesRecon results


Reference manual: bayesRecon.pdf
Vignettes: Probabilistic Reconciliation via Conditioning with 'bayesRecon'
Properties of the reconciled distribution via conditioning


Package source: bayesRecon_0.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): bayesRecon_0.2.0.tgz, r-oldrel (arm64): bayesRecon_0.2.0.tgz, r-release (x86_64): bayesRecon_0.2.0.tgz, r-oldrel (x86_64): bayesRecon_0.2.0.tgz
Old sources: bayesRecon archive


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