Provides a minimal, light-weight set of tools for producing nice looking maps in R, with support for map projections.  See Brown (2016) <doi:10.32614/RJ-2016-005>.

Visualise admixture as pie charts on a projected map, admixture as traditional structure barplots or facet barplots, and scatter plots from genotype principal components analysis. A 'shiny' app allows users to create admixture maps interactively. Jenkins TL (2024) <doi:10.1111/1755-0998.13943>.

Maps are an important tool to visualise variables distribution across different spatial objects. The mapping process requires to link the data with coordinates and then generate the correspondent map. This package provide coordinates, linking and mapping functions for an automatic, flexible and easy approach of external functions. The package provides an easy, flexible and automatic unit. Geographical coordinates are provided in the package and automatically linked with the input data to generate maps with internal provided functions or external functions. Provide an easy, flexible and automatic approach to potentially download updated coordinates, to link statistical units with coordinates and to aggregate variables based on the spatial hierarchy of units. The object returned from the package can be used for thematic maps with the build-in functions provided in mapping or with other packages already available.

Easily create functions to map between different sets of values,
  such as for re-labelling categorical variables.

Create simple maps; add sub-plots like pie plots to a map or any other plot; format, plot and export gridded data. The package was developed for displaying fisheries data but most functions can be used for more generic data visualisation.

Construction of genetic maps in autopolyploid full-sib populations. 
             Uses pairwise recombination fraction estimation as the first 
             source of information to sequentially position allelic variants 
             in specific homologous chromosomes. For situations where pairwise 
             analysis has limited power, the algorithm relies on the multilocus 
             likelihood obtained through a hidden Markov model (HMM). 
             For more detail, please see  Mollinari and Garcia (2019) 
             <doi:10.1534/g3.119.400378> and Mollinari et al. (2020) 
             <doi:10.1534/g3.119.400620>.