Title: Jackknife estimates of Kaplan-Meier estimators or integrals.
Description: The package is for computing both the original and
modified jackknife estimates of Kaplan-Meier estimators.

Title: Jackknife Estimates of Kaplan-Meier Estimators or Integrals
Description: The package is for computing both the original and modified jackknife estimates of Kaplan-Meier estimators.

Description: Computing the original and modified jackknife estimates of Kaplan-Meier estimators.

Title: Correcting Nonparametric Frontier Measurements for Outliers
person("Kleber Morais de","Sousa", role = c("aut", "cre"), email = "kleberfinancas@gmail.com"),
person("Maria da Conceicao Sampaio de", "Sousa", role = c("aut"), email = "csampaiodesousa@gmail.com"),
person("Paulo Aguiar do", "Monte", role = c("aut"), email = "pauloaguiardomonte@gmail.com")
Author: Kleber Morais de Sousa [aut, cre],
Maria da Conceicao Sampaio de Sousa [aut],
Maintainer: Kleber Morais de Sousa <kleberfinancas@gmail.com>
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." <doi:10.1007/s11123-005-4702-4>.
Imports: fBasics, Benchmarking, dplyr, ggplot2, foreach, doParallel,
reshape, tidyr, scales, parallel, graphics, plyr, rlang, utils

Title: Non-parametric Jackstraw for Principal Component Analysis
Author: Neo Christopher Chung <nc@princeton.edu>
Maintainer: Neo Christopher Chung <nc@princeton.edu>
Description: Significance test for association between variables and their

Title: Statistical Inference of Variables Driving Systematic Variation
Author: Neo Christopher Chung <nchchung@gmail.com>, John D. Storey
Description: Significance test for association between variables
Latent variables may be estimated by principal component analysis (PCA),
logistic factor analysis (LFA), and other techniques.