fit_bsts

Fits a BSTS model for a time series y, given a vector or matrix of covariates z.

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

fit_bsts function

<dl><dt>y</dt>
<dd>A numeric vector (time series response variable).</dd>
<dt>z</dt>
<dd>A numeric vector or matrix (covariates).</dd>
<dt>lags</dt>
<dd>Integer, number of lags for the autoregressive component.</dd>
<dt>MCMC.iter</dt>
<dd>Integer, number of MCMC iterations.</dd></dl>

Arguments

Fit a Bayesian Structural Time Series (BSTS) Model — fit_bsts

<dl>

<dt>y</dt>
<dd>A numeric vector (time series response variable).</dd>


<dt>z</dt>
<dd>A numeric vector or matrix (covariates).</dd>


<dt>lags</dt>
<dd>Integer, number of lags for the autoregressive component.</dd>


<dt>MCMC.iter</dt>
<dd>Integer, number of MCMC iterations.</dd>

</dl>

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fit_bsts: Fit a Bayesian Structural Time Series (BSTS) Model

Description

Usage

Value

Arguments