Provides a small set of functions for managing R environments, with defaults designed to encourage usage patterns that scale well to larger code bases. It provides: import_from(), a flexible way to assign bindings that defaults to the current environment; include(), a vectorized alternative to base::source() that also default to the current environment; and attach_eval() and attach_source(), a way to evaluate expressions in attached environments. Together, these (and other) functions pair to provide a robust alternative to base::library() and base::source().

Generation of bioclimatic rasters that are complementary to the typical 19 bioclim variables.

It contains functions for dose calculation for different routes, fitting data to probability distributions, random number generation (Monte Carlo simulation) and calculation of systemic and carcinogenic risks. For more information see the publication: Barrio-Parra et al. (2019) "Human-health probabilistic risk assessment: the role of exposure factors in an urban garden scenario" <doi:10.1016/j.landurbplan.2019.02.005>.

Set of functions to keep track and find objects in user-defined environments
    by identifying environments by name --which cannot be retrieved with the built-in function environmentName().
    The package also provides functionality to obtain simplified information about function calling chains
    and to get an object's memory address.

A plot overlying the niche of multiple species is obtained: 1) to determine the niche conditions which favor a higher species richness, 2) to create a box plot with the range of environmental variables of the species, 3) to obtain a list of species in an area of the niche selected by the user and, 4) to estimate niche overlap among the species.

Contains utilities for the analysis of protein sequences in a phylogenetic context. 
    Allows   the generation of phylogenetic trees base on protein sequences in an alignment-independent way. 
    Two different methods have been implemented. One approach is based on the frequency analysis of n-grams, 
    previously described in Stuart et al. (2002) <doi:10.1093/bioinformatics/18.1.100>. The other approach is based on the species-specific neighborhood preference around amino acids. Features include the conversion of a protein set into a vector 
    reflecting these neighborhood preferences, pairwise distances (dissimilarity) between these vectors, 
    and the generation of trees based on these distance matrices.