Author: Abdelaati Daouia <Abdelaati.Daouia@tse-fr.eu>, Thibault Laurent <thibault.laurent@univ-tlse1.fr>, Hohsuk Noh <word5810@gmail.com>
Depends: R (>= 2.9.1), splines, graphics, Rglpk, Benchmarking
Description: This package provides functions for the best known approaches to nonparametric boundary estimation.  The selected methods are concerned with empirical, smooth, unconstrained as well as restricted estimates under  both separate and multiple shape constraints.  The package also allows for Monte Carlo comparisons among these different estimation procedures, illustrating the simulation experiments by Daouia, Noh and Park (2013),  Data envelope fitting with constrained polynomial splines, TSE Working Paper, http://www.tse-fr.eu/images/doc/wp/etrie/wp_tse_449.pdf

Depends: R (>= 2.9.1), splines, quadprog, graphics, Rglpk, Benchmarking
Description: A variety of functions for the best known and most innovative approaches to nonparametric boundary estimation. The selected methods are concerned with empirical, smoothed, unrestricted as well as constrained fits under both separate and multiple shape constraints. They cover robust approaches to outliers  as well as data envelopment techniques based on piecewise polynomials, splines, local linear fitting, extreme values and kernel smoothing. The package also seamlessly allows for Monte Carlo comparisons among these different estimation methods.  Its use is illustrated via a number of empirical applications and simulated examples.

Depends: R (>= 2.9.1), graphics, stats, utils
Imports: Benchmarking, np, quadprog, Rglpk, splines

Depends: R (>= 3.3.1), graphics, stats, utils
Imports: Benchmarking, np, quadprog, Rglpk (>= 0.6-2), splines

Title: Non Parametric Test for Detecting Changes in the Copula
Description: A non parametric test for change points detection in the dependence between the components of multivariate data, with or without (multiple) changes in the marginal distributions.

Description: A non parametric test for change points detection in the dependence between the components of multivariate data, with or without (multiple) changes in the marginal distributions. The full details, justification and examples are published in Rohmer (2016) <doi:10.1016/j.spl.2016.06.026>.