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.

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.

Methods and tools for displaying and analysing
             univariate time series forecasts including exponential smoothing
             via state space models and automatic ARIMA modelling.