EBMAforecast: Estimate Ensemble Bayesian Model Averaging Forecasts using Gibbs Sampling or EM-Algorithms

Create forecasts from multiple predictions using ensemble Bayesian model averaging (EBMA). EBMA models can be estimated using an expectation maximization (EM) algorithm or as fully Bayesian models via Gibbs sampling. The methods in this package are Montgomery, Hollenbach, and Ward (2015) <doi:10.1016/j.ijforecast.2014.08.001> and Montgomery, Hollenbach, and Ward (2012) <doi:10.1093/pan/mps002>.

Version: 1.0.2
Imports: Rcpp (≥ 1.0.2), plyr, graphics, separationplot, Hmisc, abind, gtools, methods
LinkingTo: Rcpp
Published: 2020-10-28
Author: Florian M. Hollenbach ORCID iD [aut, cre], Jacob M. Montgomery [aut], Michael D. Ward [aut]
Maintainer: Florian M. Hollenbach <fhollenbach at tamu.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: <https://github.com/fhollenbach/EBMA/>
NeedsCompilation: yes
In views: TimeSeries
CRAN checks: EBMAforecast results


Reference manual: EBMAforecast.pdf
Package source: EBMAforecast_1.0.2.tar.gz
Windows binaries: r-devel: EBMAforecast_1.0.2.zip, r-release: EBMAforecast_1.0.2.zip, r-oldrel: EBMAforecast_1.0.2.zip
macOS binaries: r-release: EBMAforecast_1.0.2.tgz, r-oldrel: EBMAforecast_1.0.2.tgz
Old sources: EBMAforecast archive

Reverse dependencies:

Reverse imports: autoMrP


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