Title: Functions to Solve Quadratic Programming Problems

Author: S original by Berwin A. Turlach <Berwin.Turlach@gmail.com>
Fortran contributions from Cleve Moler (dposl/LINPACK and

Title: Quadratic Programming with Absolute Value Constraints
Authors@R: person("Bob", "Harlow", email = "rharlow86@gmail.com",role = c("aut", "cre"))
Description: Extends the quadprog package to solve quadratic programs with
absolute value constraints and absolute values in the objective function.

Title: Visualizing the SD using a Quadratic Curve
Description: 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.

Title: Sparsity by Worst-Case Quadratic Penalties
Description: This package fits classical sparse regression models with
efficient active set algorithms by solving quadratic problems.
Also provides a few methods for model selection purpose
Imports: Rcpp, RcppArmadillo, reshape2, scales, grid, parallel

efficient active set algorithms by solving quadratic problems. Also
provides a few methods for model selection purpose (cross-validation,
Imports: methods, Rcpp, RcppArmadillo, reshape2, scales, grid, parallel

Depends: R (>= 3.0.2), Rcpp, ggplot2, Matrix
Imports: reshape2, methods, scales, grid, parallel

person("Julien", "Chiquet", , "julien.chiquet@inra.fr", c("aut", "cre"))
Description: Fits classical sparse regression models with

efficient active set algorithms by solving quadratic problems as described in
<arXiv:1210.2077>. Also provides a few methods for model selection purpose

efficient active set algorithms by solving quadratic problems as described by
Grandvalet, Chiquet and Ambroise (2017) <arXiv:1210.2077>. Also provides a few
methods for model selection purpose (cross-validation, stability selection).
Suggests: testthat, spelling, lars, elasticnet, glmnet
Author: Julien Chiquet [aut, cre] (<https://orcid.org/0000-0002-3629-3429>)