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 physical activity from the National Health and Nutrition 
    Examination Survey ('NHANES') study into population-based quantiles.

Topological data analysis (TDA) is a method of data analysis that 
  uses techniques from topology to analyze high-dimensional data. Here we
  implement Mapper, an algorithm from this area developed by Singh, Mémoli and 
  Carlsson (2007) which generalizes the concept of a 
  Reeb graph <https://en.wikipedia.org/wiki/Reeb_graph>.

The Mapper algorithm from Topological Data Analysis, the steps are as follows 1. Define a filter (lens) function on the data. 2. Perform clustering within each level set. 3. Generate a complex from the clustering results.

There are three different modules: (1) model fitting and selection 
    using a set of the most commonly used equations describing developmental 
    responses to temperature helped by already existing R packages ('rTPC') 
    and nonlinear regression model functions from 'nls.multstart' 
    (Padfield et al. 2021, <doi:10.1111/2041-210X.13585>), with visualization 
    of model predictions to guide ecological criteria for model selection; 
    (2) calculation of suitability thermal limits, which consist on a 
    temperature interval delimiting the optimal performance zone or suitability; 
    and (3) climatic data extraction and visualization inspired on previous 
    research (Taylor et al. 2019, <doi:10.1111/1365-2664.13455>), with either 
    exportable rasters, static map images or html, interactive maps.

Provides a 'shiny' web application to map scores from clinical
    instruments (PANSS, SQLS, WHODAS 2.0, PHQ-8, EQ-5D-5L) to
    preference-based EQ-5D-5L health utility values using validated
    regression-based and beta-mixture mapping algorithms developed from
    Singapore population studies. Intended for use in health economic
    evaluations and cost-utility analyses.
    Methods are based on:
    Abdin et al. (2019) <doi:10.1007/s11136-018-2037-7>,
    Seow et al. (2023) <doi:10.1080/14737167.2023.2215430>,
    Abdin et al. (2021) <doi:10.1186/s12888-021-03463-0>,
    Abdin et al. (2024) <doi:10.1080/14737167.2024.2376100>.