An extensible framework for developing species distribution
    models using individual and community-based approaches, generate ensembles of
    models, evaluate the models, and predict species potential distributions in
    space and time. For more information, please check the following paper:
    Naimi, B., Araujo, M.B. (2016) <doi:10.1111/ecog.01881>.

Screen for and analyze non-linear sparse direct effects in the presence of unobserved confounding using the spectral deconfounding techniques (Ćevid, Bühlmann, and Meinshausen (2020)<jmlr.org/papers/v21/19-545.html>, Guo, Ćevid, and Bühlmann (2022) <doi:10.1214/21-AOS2152>). These methods have been shown to be a good estimate for the true direct effect if we observe many covariates, e.g., high-dimensional settings, and we have fairly dense confounding. Even if the assumptions are violated, it seems like there is not much to lose, and the deconfounded models will, in general, estimate a function closer to the true one than classical least squares optimization. 'SDModels' provides functions SDAM() for Spectrally Deconfounded Additive Models (Scheidegger, Guo, and Bühlmann (2025) <doi:10.1145/3711116>) and SDForest() for Spectrally Deconfounded Random Forests (Ulmer, Scheidegger, and Bühlmann (2025) <doi:10.1080/10618600.2025.2569602>).

Terrestrial and marine predictors for species distribution modelling
    from multiple sources, including WorldClim <https://www.worldclim.org/>,,
    ENVIREM <https://envirem.github.io/>, Bio-ORACLE <https://bio-oracle.org/>
    and MARSPEC <http://www.marspec.org/>.

Implements spatial and spatiotemporal GLMMs (Generalized Linear
    Mixed Effect Models) using 'TMB', 'fmesher', and the SPDE (Stochastic Partial
    Differential Equation) Gaussian Markov random field approximation to
    Gaussian random fields. One common application is for spatially explicit
    species distribution models (SDMs).
    See Anderson et al. (2024) <doi:10.1101/2022.03.24.485545>.

User-friendly framework that enables the training and the
    evaluation of species distribution models (SDMs). The package implements
    functions for data driven variable selection and model tuning and includes
    numerous utilities to display the results. All the functions used to select
    variables or to tune model hyperparameters have an interactive real-time
    chart displayed in the 'RStudio' viewer pane during their execution.