Probabilistic Reconciliation via Conditioning R package bayesRecon version 1.0.2 CRAN Task View: TimeSeries (https://CRAN.R-project.org/view=TimeSeries) 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) <https://proceedings.mlr.press/v244/zambon24a.html>, analytical reconciliation with Bayesian treatment of the covariance matrix (Carrara et al., 2025) <doi: 10.48550/arXiv.2506.19554>. Software Depends: R (>= 4.1.0) Imports: stats, utils, lpSolve (>= 5.6.18), nloptr Suggests: knitr, rmarkdown, forecast, glarma, scoringRules, ggplot2, testthat (>= 3.0.0) Dario Azzimonti <dario.azzimonti@gmail.com> Comprehensive R Archive Network (CRAN) Dario Azzimonti [aut, cre] (ORCID: <https://orcid.org/0000-0001-5080-3061>), Lorenzo Zambon [aut] (ORCID: <https://orcid.org/0000-0002-8939-993X>), Stefano Damato [aut] (ORCID: <https://orcid.org/0009-0001-0225-3736>), Nicolò Rubattu [aut] (ORCID: <https://orcid.org/0000-0002-2703-1005>), Giorgio Corani [aut] (ORCID: <https://orcid.org/0000-0002-1541-8384>) LGPL (>= 3) 2026-08-03 application/tgz https://CRAN.R-project.org/package=bayesRecon doi:10.32614/CRAN.package.bayesRecon