Mapping Averaged Pairwise Information (MAPI) is an
    exploratory method providing graphical representations summarizing the
    spatial variation of pairwise metrics (eg. distance, similarity
    coefficient, ...) computed between georeferenced samples.

Facilitates the incorporation of biological processes in biogeographical analyses. It offers conveniences in fitting, comparing and extrapolating models of biological processes such as physiology and phenology. These spatial extrapolations can be informative by themselves, but also complement traditional correlative species distribution models, by mixing environmental and process-based predictors. Caetano et al (2020) <doi:10.1111/oik.07123>.

Regularly spaced grids containing continuous data are transformed
    to contour polygons. A grid can be defined by a data.frame (x, y, value),
    an 'sf' object or a raster from 'terra'.

A genetic analysis tool and variance component 
    model for identifying marginal epistasis between pathways 
    and the rest of the genome. 'MAPITR' uses as input a matrix 
    of genotypes, a vector of phenotypes, and a list of 
    pathways. 'MAPITR' then iteratively tests each pathway for 
    epistasis between any variants within the pathway versus 
    any variants remaining in the rest of the genome. 'MAPITR'
    returns results in the form of p-values for every pathway 
    indicating whether the null model of there being no 
    epistatic interactions between a pathway and the rest of 
    the genome can be rejected.

Create legends for maps and other graphics. Thematic maps need to
    be accompanied by legible legends to be fully comprehensible. This package
    offers a wide range of legends useful for cartography, some of which may
    also be useful for other types of graphics.

Biodiversity areas, especially primary forest, serve a
    multitude of functions for local economy, regional functionality of
    the ecosystems as well as the global health of our planet. Recently,
    adverse changes in human land use practices and climatic responses to
    increased greenhouse gas emissions, put these biodiversity areas under
    a variety of different threats. The present package helps to analyse a
    number of biodiversity indicators based on freely available
    geographical datasets. It supports computational efficient routines
    that allow the analysis of potentially global biodiversity portfolios.
    The primary use case of the package is to support evidence based
    reporting of an organization's effort to protect biodiversity areas
    under threat and to identify regions were intervention is most duly
    needed.