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.

Species Identification using DNA Barcodes Integrated with 
             Environmental Niche Models.

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.

Provides functions complementary to packages 'nicheROVER' and 'SIBER'
        allowing the user to extract Bayesian estimates from data objects created 
        by the packages 'nicheROVER' and 'SIBER'. Please see the following 
        publications for detailed methods on 'nicheROVER' and 'SIBER' 
        Hansen et al. (2015) <doi:10.1890/14-0235.1>, 
        Jackson et al. (2011) <doi:10.1111/j.1365-2656.2011.01806.x>, and
        Layman et al. (2007) <doi:10.1890/0012-9658(2007)88[42:CSIRPF]2.0.CO;2>,
        respectfully.