Evaluation for density and distribution function of convolution of gamma
    distributions in R. Two related exact methods and one approximate method are
    implemented with efficient algorithm and C++ code. A quick guide for choosing
    correct method and usage of this package is given in package vignette. For the
    detail of methods used in this package, we refer the user to
    Mathai(1982)<doi:10.1007/BF02481056>,
    Moschopoulos(1984)<doi:10.1007/BF02481123>,
    Barnabani(2017)<doi:10.1080/03610918.2014.963612>,
    Hu et al.(2020)<doi:10.1007/s00180-019-00924-9>.

Reconstruct networks from multi-omics data sets with the
    collaborative graphical lasso (coglasso) algorithm described in Albanese, A.,
    Kohlen, W., and Behrouzi, P. (2024) <arXiv:2403.18602>. Build multiple networks
    using the coglasso() function, select the best one with stars_coglasso().

Functions for building cognitive maps based on qualitative data. Inputs are textual sources (articles, transcription of qualitative interviews of agents,...). These sources have been coded using relations and are linked to (i) a table describing the variables (or concepts) used for the coding and (ii) a table describing the sources (typology of agents, ...). Main outputs are Individual Cognitive Maps (ICM), Social Cognitive Maps (all sources or group of sources) and a list of quotes linked to relations. This package is linked to the work done during the PhD of Frederic M. Vanwindekens (CRA-W / UCL) hold the 13 of May 2014 at University of Louvain in collaboration with the Walloon Agricultural Research Centre (project MIMOSA, MOERMAN fund).

Provides authentication for Shiny applications using 'Amazon Cognito' ( <https://aws.amazon.com/es/cognito/>).

Computing Cohen's d_p in any experimental designs (between-subject, within-subject, and single-group design). Cousineau (2022) <https://github.com/dcousin3/CohensdpLibrary>; Cohen (1969, ISBN: 0-8058-0283-5).

This software tool is designed to generate new cohorts utilizing data from 
    previously instantiated cohorts. It employs interval algebra operators such as UNION, 
    INTERSECT, and MINUS to manipulate the data within the instantiated cohorts and 
    create new cohorts.