| 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) |
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