For cleaning and analysis of graphs, such as animal closing force 
    measurements. 
    'forceR' was initially written and optimized to deal with insect bite force 
    measurements, but can be used for any time series. Includes a full workflow 
    to load, plot and crop data, correct amplifier and baseline drifts, 
    identify individual peak shapes (bites), rescale (normalize) peak curves, 
    and find best polynomial fits to describe and analyze force curve shapes.

Provides an interface to the 'FORCIS' database 
    (Chaabane et al. (2024) <doi:10.5281/zenodo.7390791>) on global foraminifera
    distribution. This package allows to download and to handle 'FORCIS' data.
    It is part of the FRB-CESAB working group FORCIS.
    <https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/forcis/>.

Feature Ordering by Integrated R square Dependence (FORD) is a variable selection algorithm based on the new measure of dependence: Integrated R2 Dependence Coefficient (IRDC). For more information, see the paper: Azadkia and Roudaki (2025),"A New Measure Of Dependence: Integrated R2" <doi:10.48550/arXiv.2505.18146>.

Support for the foreach looping construct.  Foreach is an
        idiom that allows for iterating over elements in a collection,
        without the use of an explicit loop counter.  This package in
        particular is intended to be used for its return value, rather
        than for its side effects.  In that sense, it is similar to the
        standard lapply function, but doesn't require the evaluation
        of a function.  Using foreach without side effects also
        facilitates executing the loop in parallel.

Implementation of Forecastable Component Analysis ('ForeCA'),
    including main algorithms and auxiliary function (summary, plotting, etc.) to
    apply 'ForeCA' to multivariate time series data. 'ForeCA' is a novel dimension
    reduction (DR) technique for temporally dependent signals. Contrary to other
    popular DR methods, such as 'PCA' or 'ICA', 'ForeCA' takes time dependency
    explicitly into account and searches for the most ''forecastable'' signal.
    The measure of forecastability is based on the Shannon entropy of the spectral
    density of the transformed signal.