Returns a data frame with the names of the input data points and hex colors (or CIELab coordinates). Data can be mapped to colors for use in data visualization. It optimally maps data points into a polygon that represents the CIELab colour space. Since Euclidean distance approximates relative perceptual differences in CIELab color space, the result is a color encoding that aims to capture much of the structure of the original data.

Find and import datasets from the
    University of California Irvine Machine Learning (UCI ML) Repository into R.
    Supports working with data from UCI ML repository inside of R scripts, notebooks, 
    and 'Quarto'/'RMarkdown' documents. Access the UCI ML repository directly at
    <https://archive.ics.uci.edu/>.

Clustering and classification inference for high dimension low sample size (HDLSS)
    data with U-statistics. The package contains implementations of nonparametric statistical
    tests for sample homogeneity, group separation, clustering, and classification of 
    multivariate data. The methods have high statistical power and are tailored for data
    in which the dimension L is much larger than sample size n. See Gabriela B. Cybis,
    Marcio Valk and Sílvia RC Lopes (2018) <doi:10.1080/00949655.2017.1374387>, Marcio 
    Valk and Gabriela B. Cybis (2020) <doi:10.1080/10618600.2020.1796398>, Debora Z. Bello, Marcio 
    Valk and Gabriela B. Cybis (2021) <arXiv:2106.09115>.

An algorithm for general-purpose unconstrained non-linear optimization.
             The algorithm is of quasi-Newton type with BFGS updating of the inverse
             Hessian and soft line search with a trust region type monitoring of the
             input to the line search algorithm. The interface of 'ucminf' is
             designed for easy interchange with 'optim'.

Comprehensive analysis and forecasting 
             of univariate time series using automatic 
             time series models of many kinds.
             Harvey AC (1989) <doi:10.1017/CBO9781107049994>.
             Pedregal DJ and Young PC (2002) <doi:10.1002/9780470996430>.
             Durbin J and Koopman SJ (2012) <doi:10.1093/acprof:oso/9780199641178.001.0001>.
             Hyndman RJ, Koehler AB, Ord JK, and Snyder RD (2008) <doi:10.1007/978-3-540-71918-2>.
             Gómez V, Maravall A (2000) <doi:10.1002/9781118032978>.
             Pedregal DJ, Trapero JR and Holgado E (2024) <doi:10.1016/j.ijforecast.2023.09.004>.