Computation of the multivariate marine recovery index, including functions for data visualization and ecological diagnostics of marine ecosystems. The computational details are described in the original publication. Reference: Chauvel, N., Grall, J., Thiébaut, E., Houbin, C., Pezy, J.P. (in press). "A general-purpose Multivariate Marine Recovery Index for quantifying the influence of human activities on benthic habitat ecological status". Ecological Indicators.

Tools for model selection and model averaging with support for a 
             wide range of statistical models. Automated model selection through
             subsets of the maximum model, with optional constraints for model 
             inclusion. Averaging of model parameters and predictions based on 
             model weights derived from information criteria (AICc and alike) 
             or custom model weighting schemes.

Multidimensional unfolding using Schoenemann's algorithm for metric
   and Procrustes rotation of unfolding results.

Provides easy access to, and manipulation of, the Munsell 
    colours. Provides a mapping between Munsell's 
    original notation (e.g. "5R 5/10") and hexadecimal strings suitable 
    for use directly in R graphics. Also provides utilities 
    to explore slices through the Munsell colour tree, to transform 
    Munsell colours and display colour palettes.

Methods for interpolating data in the Munsell color system following the ASTM D-1535 standard. Hues and chromas with decimal values can be interpolated and converted to/from the Munsell color system and CIE xyY, CIE XYZ, CIE Lab, CIE Luv, or RGB.  Includes ISCC-NBS color block lookup.  Based on the work by Paul Centore, "The Munsell and Kubelka-Munk Toolbox".

Implementation of custom 'tidymodels' metrics for multi-class
    prediction models with a single negative class. Currently are implemented
    macro-average sensitivity and specificity as in Mortaz, Ebrahim (2020)
    "Imbalance accuracy metric for model selection in multi-class imbalance
    classification problems” <doi:10.1016/j.knosys.2020.106490> and a
    generalized weighted Youden index as in Li, D.L., Shen F., Yin Y., Peng
    J.X and Chen P.Y. (2013) “Weighted Youden index and its
    two-independent-sample comparison based on weighted sensitivity and
    specificity” <doi:10.3760/cma.j.issn.0366-6999.20123102>.