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. (2025) <doi:10.18637/jss.v115.i02>.

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.

A software package help user to create virtual species for species distribution modelling. It includes
    several methods to help user to create virtual species distribution map.
    Those maps can be used for Species Distribution Modelling (SDM) study. SDM use
    environmental data for sites of occurrence of a species to predict all the sites
    where the environmental conditions are suitable for the species to persist, and
    may be expected to occur.

Spatial model calculation for static and dynamic panel data models, weights matrix creation and  Bayesian model comparison.
  Bayesian model comparison methods were described by 'LeSage' (2014) <doi:10.1016/j.spasta.2014.02.002>.
  The 'Lee'-'Yu' transformation approach is described in 'Yu', 'De Jong' and 'Lee' (2008) <doi:10.1016/j.jeconom.2008.08.002>, 'Lee' and 'Yu' (2010) <doi:10.1016/j.jeconom.2009.08.001> and 'Lee' and 'Yu' (2010) <doi:10.1017/S0266466609100099>.