Mixed-type reconciliation algorithms

.Rbuildignore

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 ^doc$
 ^Meta$
 ^data-raw$
+^\./tests/testthat/dataForTests/generate_weeklyTestData\.R$

DESCRIPTION

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 Package: bayesRecon
 Type: Package
-Date: 2023-12-19
+Date: 2024-05-29
 Title: Probabilistic Reconciliation via Conditioning
-Version: 0.2.0
+Version: 0.3.0
 Authors@R: c(person(given = "Dario",
              family = "Azzimonti",
              role = c("aut","cre"),
@@ -24,7 +24,15 @@ Authors@R: c(person(given = "Dario",
              email = "[email protected]",
              comment = c(ORCID = "0000-0002-1541-8384")))
 Maintainer: Dario Azzimonti <[email protected]>
-Description: 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>.
+Description: 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). 
 License: LGPL (>= 3)
 Depends: R (>= 4.1.0)
 Imports:
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 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown=TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 Suggests: 
     knitr,
     rmarkdown,

NAMESPACE

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 # Generated by roxygen2: do not edit by hand
 
+export(PMF.get_mean)
+export(PMF.get_quantile)
+export(PMF.get_var)
+export(PMF.sample)
+export(PMF.summary)
 export(get_reconc_matrices)
 export(reconc_BUIS)
 export(reconc_MCMC)
+export(reconc_MixCond)
+export(reconc_TDcond)
 export(reconc_gaussian)
 export(schaferStrimmer_cov)
 export(temporal_aggregation)

NEWS.md

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+# bayesRecon 0.3.0
+
+* Added `reconc_MixCond`, the implementation of Mixed conditioning for the reconciliation of mixed-type hierarchical forecasts.
+
+* Added `reconc_TDcond`, the implementation of Top-down conditioning for the reconciliation of mixed-type hierarchical forecasts.
+
+* Vignette "Reconciliation of M5 hierarchy with mixed-type forecasts".
+
+* Added functions `PMF.get_mean`, `PMF.get_quantile`, `PMF.get_var`, `PMF.sample`, `PMF.summary` that return point estimates and samples from a probability mass function object.
+
+* Added the dataset `M5_CA1_basefc` which contains the base forecasts for the store "CA1" of the hierarchical time series in the M5 competition. 
+
 # bayesRecon 0.2.0
 
-* Vignette "Properties of the reconciled distribution via conditioning"
+* Vignette "Properties of the reconciled distribution via conditioning".
 
-* Added option in `reconc_BUIS` to pass some base forecast as parameters and some as samples
+* Added option in `reconc_BUIS` to pass some base forecast as parameters and some as samples.
 
 * Added option in `reconc_BUIS` to input a list of distributions instead of a string.