Estimates networks of conditional dependencies (Gaussian graphical models) from multiple classes of data (similar but not exactly, i.e. measurements on different equipment, in different locations or for various sub-types). Package also allows to generate simulation data and evaluate the performance. Implementation of the method described in Angelini, De Canditiis and Plaksienko (2022) <doi:10.3390/math10213983>.

Provides statistical methods for auditing as implemented in JASP
    for Audit (Derks et al., 2021 <doi:10.21105/joss.02733>). First, the package
    makes it easy for an auditor to plan a statistical sample, select the sample
    from the population, and evaluate the misstatement in the sample compliant
    with international auditing standards. Second, the package provides
    statistical methods for auditing data, including tests of digit
    distributions and repeated values. Finally, the package includes methods for
    auditing algorithms on the aspect of fairness and bias. Next to classical
    statistical methodology, the package implements Bayesian equivalents of
    these methods whose statistical underpinnings are described in Derks et al.
    (2021) <doi:10.1111/ijau.12240>, Derks et al. (2024)
    <doi:10.2308/AJPT-2021-086>, and Derks et al. (2022)
    <doi:10.31234/osf.io/8nf3e>.

Offers procedures to support financial-economic time series modelling and enhanced procedures for computing the investment performance indices of Bacon (2004) <DOI:10.1002/9781119206309>.

Provides functions to extract joint planes from 3D triangular mesh derived
             from point cloud and makes data available for structural analysis.

The Joint Graphical Lasso is a generalized method for
        estimating Gaussian graphical models/ sparse inverse covariance
        matrices/ biological networks on multiple classes of data.  We
        solve JGL under two penalty functions: The Fused Graphical
        Lasso (FGL), which employs a fused penalty to encourage inverse
        covariance matrices to be similar across classes, and the Group
        Graphical Lasso (GGL), which encourages similar network
        structure between classes.  FGL is recommended over GGL for
        most applications. Reference: Danaher P, Wang P, Witten DM. (2013) 
        <doi:10.1111/rssb.12033>.