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jackstrap  

Correcting Nonparametric Frontier Measurements for Outliers
View on CRAN: Click here


Download and install jackstrap package within the R console
Install from CRAN:
install.packages("jackstrap")

Install from Github:
library("remotes")
install_github("cran/jackstrap")

Install by package version:
library("remotes")
install_version("jackstrap", "0.1.0")



Attach the package and use:
library("jackstrap")
Maintained by
Kleber Morais de Sousa
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-06-09
Latest Update: 2020-06-09
Description:
Provides method used to check whether data have outlier in efficiency measurement of big samples with data envelopment analysis (DEA). In this jackstrap method, the package provides two criteria to define outliers: heaviside and k-s test. The technique was developed by Sousa and Stosic (2005) "Technical Efficiency of the Brazilian Municipalities: Correcting Nonparametric Frontier Measurements for Outliers." .
How to cite:
Kleber Morais de Sousa (2020). jackstrap: Correcting Nonparametric Frontier Measurements for Outliers. R package version 0.1.0, https://cran.r-project.org/web/packages/jackstrap. Accessed 03 Dec. 2024.
Previous versions and publish date:
No previous versions
Other packages that cited jackstrap R package
View jackstrap citation profile
Other R packages that jackstrap depends, imports, suggests or enhances
Complete documentation for jackstrap
Functions, R codes and Examples using the jackstrap R package
Some associated functions: hist_jack_ks . hist_jack_step . jackstrap . jackstrap_ks . municipalities . plot_jackstrap_ks . summary_jackstrap . 
Some associated R codes: data.R . globals.R . hist_jack_ks.R . hist_jack_step.R . jackstrap.R . jackstrap_ks.R . plot_jackstrap_ks.R . summary_jackstrap.R .  Full jackstrap package functions and examples
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