Implements species distribution modeling and ecological niche
	modeling, including: bias correction, spatial cross-validation, model
	evaluation, raster interpolation, biotic "velocity" (speed and
	direction of movement of a "mass" represented by a raster), interpolating
	across a time series of rasters, and use of spatially imprecise records.
	The heart of the package is a set of "training" functions which
	automatically optimize model complexity based number of available
	occurrences. These algorithms include MaxEnt, MaxNet, boosted regression
	trees/gradient boosting machines, generalized additive models,
	generalized linear models, natural splines, and random forests. To enhance
	interoperability with other modeling packages, no new classes are created.
	The package works with 'PROJ6' geodetic objects and coordinate reference
	systems.

Constructing niche models and analyzing patterns of niche evolution.  Acts as an interface for many popular modeling algorithms, and allows users to conduct Monte Carlo tests to address basic questions in evolutionary ecology and biogeography.  Warren, D.L., R.E. Glor, and M. Turelli (2008) <doi:10.1111/j.1558-5646.2008.00482.x> Glor, R.E., and D.L. Warren (2011) <doi:10.1111/j.1558-5646.2010.01177.x>  Warren, D.L., R.E. Glor, and M. Turelli (2010) <doi:10.1111/j.1600-0587.2009.06142.x> Cardillo, M., and D.L. Warren (2016) <doi:10.1111/geb.12455> D.L. Warren, L.J. Beaumont, R. Dinnage, and J.B. Baumgartner (2019) <doi:10.1111/ecog.03900>.

An algorithmic framework for measuring feature importance,
    outlier detection, model applicability domain evaluation,
    and ensemble predictive modeling with (sparse)
    partial least squares regressions.

A flexible tool for enrichment analysis based on user-defined sets. It allows users to perform over-representation analysis of the custom sets among any specified ranked feature list, hence making enrichment analysis applicable to various types of data from different scientific fields. 'EnrichIntersect' also enables an interactive means to visualize identified associations based on, for example, the mix-lasso model (Zhao et al., 2022 <doi:10.1016/j.isci.2022.104767>) or similar methods.