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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>.
| Version: |
1.0.32 |
| Imports: |
Rcpp (≥ 1.0.2), plyr, graphics, separationplot, Hmisc, abind, gtools, methods, glue |
| LinkingTo: |
Rcpp |
| Published: |
2024-03-20 |
| Author: |
Florian M. Hollenbach
 [aut, cre],
Jacob M. Montgomery [aut],
Michael D. Ward [aut] |
| Maintainer: |
Florian M. Hollenbach <fho.egb at cbs.dk> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/fhollenbach/EBMA/ |
| NeedsCompilation: |
yes |
| In views: |
TimeSeries |
| CRAN checks: |
EBMAforecast results |
Documentation:
Downloads:
Reverse dependencies:
Linking:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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