Provides interface to the 'Copernicus Data Space Ecosystem' API 
    <https://dataspace.copernicus.eu/analyse/apis>, mainly for searching the catalog of available
    data from Copernicus Sentinel missions and obtaining the images for just the area of interest 
    based on selected spectral bands. The package uses the 'Sentinel Hub' REST API interface
    <https://dataspace.copernicus.eu/analyse/apis/sentinel-hub> that provides access to various 
    satellite imagery archives. It allows you to access raw satellite data, rendered images, 
    statistical analysis, and other features.

Deriving skill structures from skill assignment 
    data for courses (sets of learning objects).

Cuddy-Della valle index gives the degree of instability present in the data by accommodating the effect of a trend. The adjusted R squared value of the best fitted model is chosen. The index is obtained by multiplying the coefficient of variation with square root of one minus the adjusted R-squared value. This package has been developed using concept of Shankar et al. (2022)<doi:10.3389/fsufs.2023.1208898>.

Provides tools for sampling from a conditional copula density decomposed via 
     Pair-Copula Constructions as C- or D- vine. Here, the vines which can be used for such a 
     sampling are those which sample as first the conditioning variables (when following the 
     sampling algorithms shown in Aas et al. (2009) <DOI:10.1016/j.insmatheco.2007.02.001>). 
     The used sampling algorithm is presented and discussed in Bevacqua et al. (2017) 
     <DOI:10.5194/hess-2016-652>, and it is a modified version of that from Aas et al. (2009) 
     <DOI:10.1016/j.insmatheco.2007.02.001>. A function is available to select the best vine 
     (based on information criteria) among those which allow for such a conditional sampling. 
     The package includes a function to compare scatterplot matrices and pair-dependencies of 
     two multivariate datasets.

Analysis and visualization of cellular energetics data from
    Agilent Seahorse XF96. Cellular energetics is how cells make, use, and
    distribute units of energy (primarily ATP). Measuring real-time cellular
    energetics is essential to understanding a tissue or cell’s bioenergetic
    state and fuel dependencies. The Seahorse machine measures a cell’s or
    matrix’s oxygen consumption rate (OCR) – a proxy of oxidative
    phosphorylation – and extracellular acidification rate – a proxy of
    glycolysis. This package offers flexible and fast analysis and plotting
    capabilities for such data using the methods described by Mookerjee et al.
    (2017) <doi:10.1074/jbc.m116.774471>.