Nonparametric Boundary Regression

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.


Reference manual

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1.6 by Thibault Laurent, 3 years ago

Browse source code at

Authors: Abdelaati Daouia <[email protected]> , Thibault Laurent <[email protected]> , Hohsuk Noh <[email protected]>

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Benchmarking, np, quadprog, Rglpk, splines

Depends on graphics, stats, utils

See at CRAN