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., 2024) <doi:10.1016/j.ijforecast.2023.04.003>, Bottom-Up Importance Sampling (Zambon et al., 2024) <doi:10.1007/s11222-023-10343-y>, 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).

Version:	0.3.2
Depends:	R (≥ 4.1.0)
Imports:	stats, utils, lpSolve (≥ 5.6.18)
Suggests:	knitr, rmarkdown, forecast, glarma, scoringRules, testthat (≥ 3.0.0)
Published:	2024-11-04
DOI:	10.32614/CRAN.package.bayesRecon
Author:	Dario Azzimonti [aut, cre], Nicolò Rubattu [aut], Lorenzo Zambon [aut], Giorgio Corani [aut]
Maintainer:	Dario Azzimonti <dario.azzimonti at gmail.com>
BugReports:	https://github.com/IDSIA/bayesRecon/issues
License:	LGPL (≥ 3)
URL:	https://github.com/IDSIA/bayesRecon
NeedsCompilation:	no
Materials:	README NEWS
In views:	TimeSeries
CRAN checks:	bayesRecon results

Documentation:

Reference manual:	bayesRecon.pdf
Vignettes:	Probabilistic Reconciliation via Conditioning with 'bayesRecon' (source, R code) Reconciliation of M5 hierarchy with mixed-type forecasts (source, R code) Properties of the reconciled distribution via conditioning (source, R code)

Downloads:

Package source:	bayesRecon_0.3.2.tar.gz
Windows binaries:	r-devel: bayesRecon_0.3.2.zip, r-release: bayesRecon_0.3.2.zip, r-oldrel: bayesRecon_0.3.2.zip
macOS binaries:	r-release (arm64): bayesRecon_0.3.2.tgz, r-oldrel (arm64): bayesRecon_0.3.2.tgz, r-release (x86_64): bayesRecon_0.3.2.tgz, r-oldrel (x86_64): bayesRecon_0.3.2.tgz
Old sources:	bayesRecon archive

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