dynamic.theta.gumbel

Computes the Gumbel copula parameter dynamically based on lagged values and covariates.

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

dynamic.theta.gumbel function

<dl><dt>params</dt>
<dd>Numeric vector of parameters: omega, alpha, and gamma coefficients.</dd>
<dt>lagged_theta</dt>
<dd>Numeric, the previous theta value.</dd>
<dt>X_t</dt>
<dd>Numeric vector or matrix of covariates at time t.</dd></dl>

Arguments

Compute Dynamic Gumbel Copula Parameter — dynamic.theta.gumbel

<dl>

<dt>params</dt>
<dd>Numeric vector of parameters: omega, alpha, and gamma coefficients.</dd>


<dt>lagged_theta</dt>
<dd>Numeric, the previous theta value.</dd>


<dt>X_t</dt>
<dd>Numeric vector or matrix of covariates at time t.</dd>

</dl>

dynamic.theta.gumbel: Compute Dynamic Gumbel Copula Parameter

Description

Usage

Value

Arguments