init_params_full

Initial Parameters for 2D Pseudo-Loglikelihood Estimation

datasets

Provides functions to model and forecast crop yields using a spatial temporal conditional copula approach.
The package incorporates extreme weather covariates and Bayesian Structural Time Series models to analyze crop
yield dependencies across multiple regions. Includes tools for fitting, simulating, and visualizing results.
This method build upon established R packages, including 'Hofert' 'et' 'al'. (2025) <doi:10.32614/CRAN.package.copula>,
'Scott' (2024) <doi:10.32614/CRAN.package.bsts>, and 'Stephenson' 'et' 'al'. (2024) <doi:10.32614/CRAN.package.evd>.

Yongkun Li

STCCGEV

Conditional Copula Model for Crop Yield Forecasting

Marie Michaelides

Mélina Mailhot

init_params_full function

A numeric vector of length \((2+M+4*D*M)\) where:<dl>
<dt>omega</dt>
<dd>Baseline autoregressive coefficient.</dd><dt>alpha</dt>
<dd>Parameter controlling variance.</dd><dt>gamma1, gamma2, gamma3</dt>
<dd>Coefficients related to external factors.</dd><dt>phi_gev</dt>
<dd>AR(1) coefficient for GEV.</dd><dt>sigma_mu</dt>
<dd>Std dev of innovations for AR(1) process for GEV.</dd><dt>sigma_gev</dt>
<dd>GEV scale parameter for GEV.</dd><dt>xi_gev</dt>
<dd>GEV shape parameter for GEV.</dd>
</dl>

Format

Initial Parameters for 2D Pseudo-Loglikelihood Estimation — init_params_full

A numeric vector of length \((2+M+4*D*M)\) where:<dl>
<dt>omega</dt>
<dd>Baseline autoregressive coefficient.</dd>

<dt>alpha</dt>
<dd>Parameter controlling variance.</dd>

<dt>gamma1, gamma2, gamma3</dt>
<dd>Coefficients related to external factors.</dd>

<dt>phi_gev</dt>
<dd>AR(1) coefficient for GEV.</dd>

<dt>sigma_mu</dt>
<dd>Std dev of innovations for AR(1) process for GEV.</dd>

<dt>sigma_gev</dt>
<dd>GEV scale parameter for GEV.</dd>

<dt>xi_gev</dt>
<dd>GEV shape parameter for GEV.</dd>


</dl>

init_params_full: Initial Parameters for 2D Pseudo-Loglikelihood Estimation

Description

Usage

Arguments

Format