Efficiently implements the Graphical Lasso algorithm,
            utilizing the 'Armadillo' 'C++' library for rapid computation. This algorithm 
            introduces an L1 penalty to derive sparse inverse covariance matrices from 
            observations of multivariate normal distributions. Features include the 
            generation of random and structured sparse covariance matrices, beneficial 
            for simulations, statistical method testing, and educational purposes in 
            graphical modeling. A unique function for regularization parameter selection 
            based on predefined sparsity levels is also offered, catering to users with
            specific sparsity requirements in their models. The methodology for sparse 
            inverse covariance estimation implemented in this package is based on the 
            work of Friedman, Hastie, and Tibshirani (2008) <doi:10.1093/biostatistics/kxm045>.

Performs variable selection in high-dimensional sparse GLARMA models. For further details we refer the reader to the paper Gomtsyan et al. (2020), <arXiv:2007.08623v1>.

Interacts with the 'Glassdoor' API
    <https://www.glassdoor.com/developer/index.htm>. Allows the user to
    search job statistics, employer statistics, and job progression,
    where 'Glassdoor' provides a breakdown of other jobs a person did
    after their current one.

Estimation of a sparse inverse covariance matrix using a lasso (L1)
             penalty. Facilities are provided for estimates along a path of values
	     for the regularization parameter.

A fast and improved implementation of the graphical LASSO.

Analyses response times and accuracies from psychological experiments with the linear ballistic accumulator (LBA) model from Brown and Heathcote (2008). The LBA model is optionally fitted with explanatory variables on the parameters such as the drift rate, the boundary and the starting point parameters. A log-link function on the linear predictors can be used to ensure that parameters remain positive when needed.