A comprehensive collection of color palettes, colormaps, and
    tools to evaluate them. See Kovesi (2015) <doi:10.48550/arXiv.1509.03700>.

Processing Chlorophyll Fluorescence & P700 Absorbance data. Four models are provided for the regression of Pi curves, which can be compared with each other in order to select the most suitable model for the data set. Control plots ensure the successful verification of each regression. Bundled output of alpha, ETRmax, Ik etc. enables fast and reliable further processing of the data.

Functions for easily reading and processing binary data files created by
    'Pamguard' (<https://www.pamguard.org/>). All functions for directly reading the
    binary data files are based on 'MATLAB' code written by Michael Oswald.

We propose a pair of summary measures for the predictive power of a prediction
    function based on a regression model. The regression model can be linear
    or nonlinear, parametric, semi-parametric, or nonparametric, and correctly
    specified or mis-specified. The first measure, R-squared, is an extension of
    the classical R-squared statistic for a linear model, quantifying the prediction
    function's ability to capture the variability of the response. The second
    measure, L-squared, quantifies the prediction function's bias for predicting the
    mean regression function. When used together, they give a complete summary of
    the predictive power of a prediction function. Please refer to Gang Li and Xiaoyan Wang (2016) <arXiv:1611.03063> for more details.

Data are partitioned (clustered) into k clusters "around medoids", which is
    a more robust version of K-means implemented in the function pam() in the 'cluster' package.
    The PAM algorithm is described in Kaufman and Rousseeuw (1990) <doi:10.1002/9780470316801>.
    Please refer to the pam() function documentation for more references.
    Clustered data is plotted as a split heatmap allowing visualisation of representative
    "group-clusters" (medoids) in the data as separated fractions of the graph while those
    "sub-clusters" are visualised as a traditional heatmap based on hierarchical clustering.