Process raw force-plate data (txt-files) by segmenting them into trials and, if needed, calculating (user-defined) descriptive 
    statistics of variables for user-defined time bins (relative to trigger onsets) for each trial. When segmenting the data a baseline 
    correction, a filter, and a data imputation can be applied if needed. Experimental data can also be processed and combined with the 
    segmented force-plate data. This procedure is suggested by Johannsen et al. (2023) <doi:10.6084/m9.figshare.22190155> and some of the 
    options (e.g., choice of low-pass filter) are also suggested by Winter (2009) <doi:10.1002/9780470549148>.

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>.

Forest Many-Objective Robust Decision Making ('FoRDM') is a R toolkit for supporting robust forest management under deep uncertainty.
    It provides a forestry-focused application of Many-Objective Robust Decision Making ('MORDM') to forest simulation outputs, 
    enabling users to evaluate robustness using regret- and 'satisficing'-based measures. 'FoRDM' identifies robust solutions, 
    generates Pareto fronts, and offers interactive 2D, 3D, and parallel-coordinate visualizations.