The 'rmoo' package is a framework for multi- and many-objective
        optimization, which allows researchers and users versatility
        in parameter configuration, as well as tools for analysis, replication
        and visualization of results. The 'rmoo' package was built as a fork of
        the 'GA' package by Luca Scrucca(2017) <DOI:10.32614/RJ-2017-008> and
        implementing the Non-Dominated Sorting Genetic Algorithms proposed
        by K. Deb's.

Provides functions for risk management and portfolio investment of securities with practical tools for data processing and plotting. Moreover, it contains functions which perform the COS Method, an option pricing method based on the Fourier-cosine series (Fang, F. (2008) <doi:10.1137/080718061>).

Provides R bindings for 'Mosaic', a declarative grammar for
    linked, data-driven visualizations backed by 'DuckDB'. The package supplies
    'htmlwidgets' and 'shiny' helpers for rendering 'Mosaic' specifications
    from R, including optional browser-side 'DuckDB-WASM' execution and
    selection export helpers for exploratory workflows.

This is a meta-package designed to support the
        installation of Rmosek (>= 6.0) and bring the optimization
        facilities of MOSEK (>= 6.0) to the R-language. The interface
        supports large-scale optimization of many kinds: Mixed-integer
        and continuous linear, second-order cone, exponential cone and
        power cone optimization, as well as continuous semidefinite
        optimization. Rmosek and the R-language are open-source
        projects. MOSEK is a proprietary product, but unrestricted
        trial and academic licenses are available.

Estimates Pareto-optimal solution for personnel selection
    with 3 objectives using Normal Boundary Intersection (NBI) algorithm
    introduced by Das and Dennis (1998) <doi:10.1137/S1052623496307510>.
    Takes predictor intercorrelations and predictor-objective relations as
    input and generates a series of solutions containing predictor weights
    as output. Accepts between 3 and 10 selection predictors. Maximum 2
    objectives could be adverse impact objectives. Partially modeled after
    De Corte (2006) TROFSS Fortran program
    <https://users.ugent.be/~wdecorte/trofss.pdf> and updated from
    'ParetoR' package described in Song et al. (2017)
    <doi:10.1037/apl0000240>. For details, see Study 3 of Zhang et al. (2023).