# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "lopensemble" in publications use:' type: software license: MIT title: 'lopensemble: Create Mixture Models From Predictive Samples' version: 0.1.2.9000 doi: 10.32614/CRAN.package.lopensemble abstract: Combines predictions from individual time series or panel data models into an ensemble using stacking (Yao, Vehtari, Simpson, and Gelman (2018) ) based on the Continuous Ranked Probability Score (CRPS) (Gneiting and Raftery (2007) ) over k-step ahead predictions. Predictions must be predictive distributions represented by samples, typically posterior predictive simulation draws from a Markov chain Monte Carlo (MCMC) algorithm. Given training data with observed values and predictive samples from different models, optimal stacking weights are computed to minimize expected cross-validation predictive error. These weights can then be used to generate samples from the mixture model by drawing from individual model predictions in the correct proportions. authors: - family-names: Bosse given-names: Nikos email: nikosbosse@gmail.com orcid: https://orcid.org/0000-0002-7750-5280 - family-names: Yao given-names: Yuling email: yy2619@columbia.edu - family-names: Abbott given-names: Sam email: contact@samabbott.co.uk orcid: https://orcid.org/0000-0001-8057-8037 - family-names: Funk given-names: Sebastian email: sebastian.funk@lshtm.ac.uk orcid: https://orcid.org/0000-0002-2842-3406 repository: https://epiforecasts.r-universe.dev commit: cd260b8a8dac089bd8d188fc32ef937f988ad296 date-released: '2026-01-30' contact: - family-names: Bosse given-names: Nikos email: nikosbosse@gmail.com orcid: https://orcid.org/0000-0002-7750-5280