This package contains routines and documentation for
        solving quadratic programming problems.

Extends the quadprog package to solve quadratic programs with
  absolute value constraints and absolute values in the objective function.

Given a dataset, the user is invited to utilize the Empirical Cumulative Distribution Function (ECDF) 
              to guess interactively the mean and the mean deviation. Thereafter,
              using the quadratic curve the user can guess the Root Mean Squared Deviation (RMSD) 
              and visualize the standard deviation (SD). 
              For details, see Sarkar and Rashid (2019)<doi:10.3126/njs.v3i0.25574>, 
              Have You Seen the Standard Deviaton?, Nepalese Journal of Statistics, Vol. 3, 1-10.

It includes test for multivariate normality, test for uniformity on the d-dimensional 
    Sphere, non-parametric two- and k-sample tests, random generation of points from the Poisson 
    kernel-based density and clustering algorithm for spherical data. For more information see
    Saraceno G., Markatou M., Mukhopadhyay R. and Golzy M. (2024)
    <doi:10.48550/arXiv.2402.02290>
    Markatou, M. and Saraceno, G. (2024) <doi:10.48550/arXiv.2407.16374>, 
    Ding, Y., Markatou, M. and Saraceno, G. (2023) <doi:10.5705/ss.202022.0347>, 
    and Golzy, M. and Markatou, M. (2020) <doi:10.1080/10618600.2020.1740713>.

It will assist the user to find simple quadratic roots from any quadratic equation.

Fits the solution paths of classical sparse regression models with efficient active set algorithms by solving small sub-problems. Include LASSO, SCAD, MCP, (Sparse) Group-LASSO, Cooperative-LASSO, (Group) LAVA, (Generalized) Fused-Lasso and (Generalized) Elastic-Net. Also provides methods for model selection purpose (information criteria, cross-validation, stability selection).

Estimate quadratic vector autoregression models with the
    strong hierarchy using the Regularization Algorithm under Marginality
    Principle (RAMP) by Hao et al. (2018)
    <doi:10.1080/01621459.2016.1264956>, compare the performance with
    linear models, and construct networks with partial derivatives.