A 'shiny' application, which allows you to perform single- and
    multi-omics analyses using your own omics datasets. After the upload 
    of the omics datasets and a metadata file, single-omics is performed
    for feature selection and dataset reduction. These datasets are used 
    for pairwise- and multi-omics analyses, where automatic tuning is 
    done to identify correlations between the datasets - the end goal 
    of the recommended 'Holomics' workflow.
    Methods used in the package were implemented in the package 'mixomics'
    by Florian Rohart,Benoît Gautier,Amrit Singh,Kim-Anh Lê Cao (2017) <doi:10.1371/journal.pcbi.1005752>
    and are described there in further detail.

Performs a homogeneity analysis (multiple correspondence analysis) and various extensions. Rank restrictions on the category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis.

Implements indirect demographic methods for estimating adult
    mortality from orphanhood data. The package includes the standard
    Brass and Hill (1973) method
    <https://scholar.google.com/scholar_lookup?&title=Estimating%20Adult%20Mortality%20from%20Orphanhood&pages=111-23&publication_year=1973&author=Brass%2CW.&author=Hill.%2CK.>,
    the regression-based approach developed by Timaeus (1992)
    <https://pubmed.ncbi.nlm.nih.gov/12317481/>, and the adjustments
    proposed by Luy (2012) <doi:10.1007/s13524-012-0101-4> for
    low-mortality populations. A relational model is used to harmonize
    estimates into comparable adult mortality indicators. The package
    also provides diagnostic tools to assess the sensitivity of results
    to assumptions about the mean age of childbearing and the choice of
    model life table family.

Provides empirically strong allometric predictions of the home-range size of most vertebrate species. Based on inputs of mean body size, taxonomic class, and optional classifications of environment and trophic level or foraging mode, 'HomeRangeR' predicts home-range size using the most appropriate model for the species selected from a collection of empirically derived vertebrate home-range allometries.