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npbr
View on CRAN: Click
here
Download and install npbr package within the R console
Install from CRAN:
install.packages("npbr")
Install from Github:
library("remotes")
install_github("cran/npbr") Install by package version:
library("remotes")
install_version("npbr", "1.8") Attach the package and use:
library("npbr")
Maintained by
Thibault Laurent
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All associated links for this package
First Published: 2013-12-12
Latest Update: 2023-03-22
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.
How to cite:
Thibault Laurent (2013). npbr: Nonparametric Boundary Regression. R package version 1.8, https://cran.r-project.org/web/packages/npbr. Accessed 26 Aug. 2026.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for npbr
Functions, R codes and Examples using
the npbr R package
Some associated functions: dea_est . green . kern_smooth . kern_smooth_bw . loc_est . loc_est_bw . npbr-package . nuclear . poly_est . post . quad_spline_est . quad_spline_kn . records .
Some associated R codes: dea_est.R . kern_smooth.R . kern_smooth_bw.R . loc_est.R . loc_est_bw.R . poly_est.R . quad_spline_est.R . quad_spline_kn.R . Full npbr package functions and examples
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