Supporting functionality to run 'caret' with spatial or spatial-temporal data. 
    'caret' is a frequently used package for model training and prediction using machine learning. 
    CAST includes functions to improve spatial or spatial-temporal modelling tasks using 'caret'. 
    It includes the newly suggested 'Nearest neighbor distance matching' cross-validation to estimate
    the performance of spatial prediction models and allows for spatial variable selection to selects
    suitable predictor variables in view to their contribution to the spatial model performance. 
    CAST further includes functionality to estimate the (spatial) area of applicability of prediction models.
    Methods are described in 
    Meyer et al. (2018) <doi:10.1016/j.envsoft.2017.12.001>; 
    Meyer et al. (2019) <doi:10.1016/j.ecolmodel.2019.108815>; 
    Meyer and Pebesma (2021) <doi:10.1111/2041-210X.13650>; 
    Milà et al. (2022) <doi:10.1111/2041-210X.13851>; 
    Meyer and Pebesma (2022) <doi:10.1038/s41467-022-29838-9>; 
    Linnenbrink et al. (2024) <doi:10.5194/gmd-17-5897-2024>; 
    Schumacher et al. (2025) <doi:10.5194/gmd-18-10185-2025>. 
    The package is described in detail in Meyer et al. (2026) <doi:10.1007/978-3-031-99665-8_11>.

Estimate sample sizes needed to capture target levels of
    genetic diversity from a population (multivariate allele frequencies)
    for applications like germplasm conservation and breeding efforts.
    Compares bootstrap samples to a full population using linear
    regression, employing the R-squared value to represent the proportion
    of diversity captured. Iteratively increases sample size until a
    user-defined target R-squared is met. Offers a parallelized R
    implementation of a previously developed 'python' method. All ploidy
    levels are supported. For more details, see Sandercock et al. (2024) <doi:10.1073/pnas.2403505121>.

Efficient phylogenetic analyses on massive phylogenies comprising up to millions of tips. Functions include pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters, simulating and fitting models of trait evolution, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format. Citation: Louca, Stilianos and Doebeli, Michael (2017) <doi:10.1093/bioinformatics/btx701>.