Implements random variables by means of S4 classes and methods.

Resolve the dependency graph of R packages at a specific time point based on the information from various 'R-hub' web services <https://blog.r-hub.io/>. The dependency graph can then be used to reconstruct the R computational environment with 'Rocker' <https://rocker-project.org>.

Provides tools for filtering occurrence records, generating alpha-hull-derived range polygons and mapping species distributions.

Tools for generation of (life-history) traits and diversity maps on hexagonal or square grids. Valcu et al.(2012) <doi:10.1111/j.1466-8238.2011.00739.x>.

Range Modeling Metadata Standards (RMMS) address three challenges: 
  they (i) are designed for convenience to encourage use, (ii) accommodate a wide 
  variety of applications, and (iii) are extensible to allow the community of range 
  modelers to steer it as needed. RMMS are based on a data dictionary that specifies 
  a hierarchical structure to catalog different aspects of the range modeling process. 
  The dictionary balances a constrained, minimalist vocabulary to improve 
  standardization with flexibility for users to provide their own values. 
  Merow et al. (2019) <DOI:10.1111/geb.12993> describe the standards in 
  more detail. Note that users who prefer to use the R package 'ecospat' can obtain it from
  <https://github.com/ecospat/ecospat>.

Used for generating randomized community matrices under
             strict range cohesion. The package can handle data where species occurrence
             are recorded across sites ordered along gradients such as elevation and latitude, as well as species occurrences recorded on spatial grids with known geographic coordinates.

A fast implementation of Random Forests, particularly suited for high
          dimensional data. Ensembles of classification, regression, survival and
          probability prediction trees are supported. Data from genome-wide association
          studies can be analyzed efficiently. In addition to data frames, datasets of
          class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix') 
          can be directly analyzed.