| clayton.theta | Compute Clayton Copula Parameter from Kendall's Tau |
| copula_list | Supported copula types |
| data | Real crop yield and meteorological data of 24 regions for Ontario, Canada from 1950 to 2022 and anticipated data from 2023 to 2100. |
| dt | Selected data from year 1950 to 2022 and covariates including txgt27, tr18, cddcold, txgt29, and tnmean for case study. |
| dynamic.rho | Compute Dynamic Gaussian Copula Correlation Parameter (rho) |
| dynamic.theta.clayton | Compute Dynamic Clayton Copula Parameter |
| dynamic.theta.frank | Compute Dynamic Frank Copula Parameter |
| dynamic.theta.gumbel | Compute Dynamic Gumbel Copula Parameter |
| dynamic.theta.joe | Compute Dynamic Joe Copula Parameter |
| fit_bsts | Fit a Bayesian Structural Time Series (BSTS) Model |
| frank.theta | Compute Frank Copula Parameter from Kendall's Tau |
| GH.theta | Compute Gumbel Copula Parameter from Kendall's Tau |
| init_params_full | Initial Parameters for 3D Pseudo-Loglikelihood Estimation |
| joe.theta | Compute Joe Copula Parameter from Kendall's Tau |
| log_likelihood_noGEV_3d | Log-Likelihood Function for 3D Copula Model |
| medoid_names | list containing Chatham-Kent, Lambton, and Wellington |
| n_test | 19 |
| n_train | 54 |
| plot_forecast | Plot Observed Data and BSTS Forecast |
| plot_forecast_compare | Compare Forecasts from Two Models |
| simul_fun_noGEV_3d | Function to optimize the full pseudo-loglikelihood and perform new forecasts |
| time | 1950-2022 |
| time_test | 2004-2022 |
| time_train | 1950-2003 |
| u | Pseudo-Observations of BSTS Residuals for Crop Yield Forecasting |
| y_test | Crop Yield Data for Testing in BSTS Models |
| y_train | Crop Yield Training Matrix |
| z_test | Standardized Covariates (Test) |
| z_train | Standardized Covariates (Training) |