Nonparametric Boundary Regression
European air controllers
Cubic spline fitting
AIC and BIC criteria for choosing the number of inter-knot segments in...
DEA, FDH and linearized FDH estimators.
Moment frontier estimator
Pickands frontier estimator
Probability-weighted moment frontier estimator
Frontier estimation via kernel smoothing
Bandwidth selection for kernel smoothing frontier estimators
Optimal in moment and Pickands frontier estimators
Local linear frontier estimator
Bandwidth selection for the local linear frontier estimator
Local maximum frontier estimators
Threshold selection for the PWM frontier estimator
Nonparametric boundary regression
Local Pickands' frontier estimator
AIC and BIC criteria for choosing the optimal degree of the polynomial...
Polynomial frontier estimators
Quadratic spline frontiers
AIC and BIC criteria for choosing the optimal number of inter-knot seg...
Optimal rho for moment and Pickands frontier estimator
Probability-weighted moment frontier estimator
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.