Provides a comprehensive framework for analyzing diversity from 
    frequency/abundance count data. Implements a wide range of classical and 
    entropy-based diversity indices, including Berger-Parker, Simpson (and 
    related variants), Shannon, Brillouin, McIntosh, Margalef, Menhinick and 
    Smith-Wilson. Supports permutation-based hypothesis tests for comparing 
    groups with respect to diversity (global and pairwise comparisons), as well 
    as confidence interval estimation using multiple bootstrap methods. 
    Includes functionality for generating diversity profiles based on 
    parametric families such as Hill numbers, Rényi entropy, and Tsallis 
    entropy. The methods are applicable to ecological community data (species 
    abundance counts) and genetic or phenotypic class frequency data.

Provides tools to sort DICOM-format medical image files, and
    convert them to NIfTI-1 format.

Curated datasets and intuitive data management functions to streamline epidemiological data workflows. It is designed to support researchers in quickly accessing clean, structured data and applying essential cleaning, summarizing, visualization, and export operations with minimal effort. Whether you're preparing a cohort for analysis or creating reports, 'DIVINE' makes the process more efficient, transparent, and reproducible.

A set of tools for empirical analysis of diversity (a number 
    and frequency of different types in a population) and similarity 
    (a number and frequency of shared types in two populations) in 
    biological or ecological systems.

Alpha and beta diversity for taxonomic (TD), functional (FD),
    and phylogenetic (PD) dimensions based on rasters. Spatial and
    temporal beta diversity can be partitioned into replacement and
    richness difference components. It also calculates standardized effect
    size for FD and PD alpha diversity and the average individual traits
    across multilayer rasters. The layers of the raster represent species,
    while the cells represent communities. Methods details can be found at
    Cardoso et al. 2022 <https://CRAN.R-project.org/package=BAT> and
    Heming et al. 2023 <https://CRAN.R-project.org/package=SESraster>.