bmetapred: Bayesian Meta-Prediction in the Presence of Prediction Error
Fits pseudo-Bayesian Model Averaging (pBMA) and Bayesian Stacking ensembles of linear
meta-models while explicitly accounting for the prediction errors of the individual models,
building on the Bayesian Stacking of predictive distributions framework of Yao et al. (2018) <doi:10.1214/17-BA1091>.
When native uncertainty estimates are unavailable, it provides tools to estimate heteroscedastic
errors via Local Polynomial Regression (LOESS). The meta-model space is explored using exhaustive
search, a Markov Chain Monte Carlo Model Composition (MC3) algorithm following Madigan and
Raftery (1994) <doi:10.1080/01621459.1994.10476894> when pBMA is selected, or a tabu-guided heuristic search with parallel tempering when bayesian stacking is selected, all powered by 'brms' (Bürkner, 2017
<doi:10.18637/jss.v080.i01>).
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[aut, cre],
Anabel Forte Deltell