Provides an implementation of simultaneous tolerance bounds (STB), useful for checking whether a numeric vector fits to a hypothetical null-distribution or not.
             Furthermore, there are functions for computing STB (bands, intervals) for random variates of linear mixed models fitted with package 'VCA'. All kinds of, possibly transformed 
             (studentized, standardized, Pearson-type transformed) random variates (residuals, random effects), can be assessed employing STB-methodology.

Provides image-related C/C++ header files from the
    'stb' single-file libraries for image loading, writing, and resizing.

A set of consistent, opinionated functions to quickly check
    function arguments, coerce them to the desired configuration, or
    deliver informative error messages when that is not possible.

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>.

Enables the creation of Chain Event Graphs over spatial areas, with an optional 'Shiny' user interface. Allows users to fully customise both the structure and underlying model of the Chain Event Graph, offering a high degree of flexibility for tailored analyses. For more details on Chain Event Graphs, see Freeman, G., & Smith, J. Q. (2011) <doi:10.1016/j.jmva.2011.03.008>, Collazo R. A., Görgen C. and Smith J. Q. (2018, ISBN:9781498729604) and Barclay, L. M., Hutton, J. L., & Smith, J. Q. (2014) <doi:10.1214/13-BA843>.

Spatio-temporal change of support (STCOS) methods are designed for
    statistical inference on geographic and time domains which differ from
    those on which the data were observed. In particular, a parsimonious class
    of STCOS models supporting Gaussian outcomes was introduced by Bradley,
    Wikle, and Holan <doi:10.1002/sta4.94>. The 'stcos' package contains tools
    which facilitate use of STCOS models.