The implementation of Markov Model Multiple Imputation
  with the application to COVID-19 scale, NIAID OS.

Framework is devoted to mining numerical association rules through the
  utilization of nature-inspired algorithms for optimization. Drawing inspiration
  from the 'NiaARM' 'Python' and the 'NiaARM' 'Julia' packages, this repository
  introduces the capability to perform numerical association rule mining in
  the R programming language.
  Fister Jr., Iglesias, Galvez, Del Ser, Osaba and Fister (2018) <doi:10.1007/978-3-030-03493-1_9>.

Color palettes based on nature inspired colours in "Sri Lanka".

Get or set UNIX priority (niceness) of running R process.

Provides a robust set of tools for researchers and modelers to
    construct and define virtual ecological niches using ellipsoid geometries.
    It enables the identification and extraction of suitable environmental
    areas, simulation of species occurrence points with various sampling
    strategies, and visualization of niche boundaries and simulated occurrences
    in both environmental and geographic space. Inspired by methodologies in
    'NicheA' and the 'virtualspecies' R package, 'nicheR' aims to streamline
    the process of niche conceptualization and data generation for ecological
    studies. Methodological and theoretical foundations are described in
    Peterson et al. (2011, ISBN:9780691136882),
    Etherington et al. (2009) <doi:10.1111/j.1365-2699.2008.02041.x>,
    Qiao et al. (2015) <doi:10.1111/ecog.01961>,
    Nunez-Penichet et al. (2021) <doi:10.21425/F5FBG52142>,
    Cobos and Peterson (2022) <doi:10.17161/bi.v17i.15985>,
    Alkishe et al. (2022) <doi:10.5194/we-22-33-2022>,
    and Leroy et al. (2015) <doi:10.1111/ecog.01388>.

Implementation of a probabilistic method to calculate 'nicheROVER' (_niche_ _r_egion and niche _over_lap) metrics using multidimensional niche indicator data (e.g., stable isotopes, environmental variables, etc.). The niche region is defined as the joint probability density function of the multidimensional niche indicators at a user-defined probability alpha (e.g., 95%).  Uncertainty is accounted for in a Bayesian framework, and the method can be extended to three or more indicator dimensions.  It provides directional estimates of niche overlap, accounts for species-specific distributions in multivariate niche space, and produces unique and consistent bivariate projections of the multivariate niche region.  The article by Swanson et al. (2015) <doi:10.1890/14-0235.1> provides a detailed description of the methodology.  See the package vignette for a worked example using fish stable isotope data.