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The EBMAforecast package allows users to combine predictions from multiple models or entities in a principled manner based on the individual components predictive performance. The package provides methods to estimate ensemble Bayesian averaging models for binary and continuous outcomes via expectation maximization (EM) algorithm or gibbs sampling. The EBMAforecast package is largely based on Montgomery et al. (2012, 2015).
The most recent version on CRAN (currently only allows estimation via EM) can be installed using: r install.packages("EBMAforecast") . A beta version of the package in development can be installed using r install_github("fhollenbach/EBMA", subdir="EBMAforecast").
Example code coming soon.
- Montgomery, Jacob M., Florian M. Hollenbach, and Michael D. Ward. 2015. Calibrating Ensemble Forecasting Models with Sparse Data in the Social Sciences. International Journal of Forecasting. 31(3). 930–942. https://doi.org/10.1016/j.ijforecast.2014.08.001
- Montgomery, Jacob M., Florian M. Hollenbach, and Michael D. Ward. 2012. Improving Predictions Using Ensemble Bayesian Model Averaging. Political Analysis. 20(3): 271–291. https://doi.org/10.1093/pan/mps002