Bayesian Error Propagation and Forecast Uncertainty Decomposition // Provides a full pipeline from regularized or standard regression models (elastic net, linear models, generalized linear models, random forests) to informed Bayesian priors, structured forecast uncertainty decomposition (parameter / environmental / residual, plus a temporal component when the model carries an autocorrelation term), and forecast shelf life analysis (the quantification of when a forecast becomes uninformative). Designed for ecological and genomic forecasting with climate or environmental covariates. Methods build on Brkner (2017) doi:10.18637/jss.v080.i01 for Bayesian regression via 'Stan', Friedman, Hastie, and Tibshirani (2010) doi:10.18637/jss.v033.i01 for elastic net regularization, Wright and Ziegler (2017) doi:10.18637/jss.v077.i01 for random forests, and Vehtari, Gelman, and Gabry (2017) doi:10.1007/s11222-016-9696-4 for leave-one-out cross- validation.