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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>.
Getting started
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Package details |
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| Author | Florian M. Hollenbach [aut, cre] (<https://orcid.org/0000-0002-9599-556X>), Jacob M. Montgomery [aut], Michael D. Ward [aut] |
| Maintainer | Florian M. Hollenbach <fho.egb@cbs.dk> |
| License | GPL (>= 2) |
| Version | 1.0.31 |
| URL | https://github.com/fhollenbach/EBMA/ |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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