Provides a collection of functions described and used in the book
    Foadi (2026, ISBN:9780750326308) "Computational Physics with R". These include
    routines for numerical differentiation, integration, differential equations,
    eigenvalue problems, Monte Carlo methods, and other algorithms relevant to
    computational physics.

A collection of functions to calculate Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps, as described in OECD Handbook on constructing composite indicators: methodology and user guide, 2008, 'Vidoli' and 'Fusco' and 'Mazziotta' <doi:10.1007/s11205-014-0710-y>, 'Mazziotta' and 'Pareto' (2016) <doi:10.1007/s11205-015-0998-2>, 'Van Puyenbroeck and 'Rogge' <doi:10.1016/j.ejor.2016.07.038> and other authors.

Provides an interactive 'shiny' web application for constructing, analyzing, 
    and visualizing composite indices from multidimensional datasets. 
    Users can upload or select indicator data, group variables into logical categories, 
    apply normalization and weighting methods (such as 'equal' or 'custom' schemes), 
    and compute aggregate composite indices. 
    The 'shiny' interface includes tools for exploring results through tables, plots, 
    and data exports, making it useful for researchers, policymakers, and analysts 
    interested in index-based evaluations.

It uses the first-order sensitivity index to measure whether the weights assigned by the creator of the composite indicator match the actual importance of the variables. Moreover, the variance inflation factor is used to reduce the set of correlated variables. In the case of a discrepancy between the importance and the assigned weight, the script determines weights that allow adjustment of the weights to the intended impact of variables. If the optimised weights are unable to reflect the desired importance, the highly correlated variables are reduced, taking into account variance inflation factor. The final outcome of the script is the calculated value of the composite indicator based on optimal weights and a reduced set of variables, and the linear ordering of the analysed objects.