Description: A tree bootstrap method for estimating uncertainty in respondent-driven samples (RDS). Quantiles are estimated by multilevel resampling in such a way that preserves the dependencies of and accounts for the high variability of the RDS process.

Title: Basic Runtime Support for Datacanvas.io
Authors@R: c(person("Xiaolin", "Zhang", role = c("aut", "cre"),
Description: Provides basic functionalities for writing a module
for http://datacanvas.io. The http://datacanvas.io is a big data
analytics platform that helps data scientists to build, manage

Title: R interface to the dieharder RNG test suite
Description: The RDieHarder packages provides an R interface to the
dieharder suite of random number generators and tests that was
developed by Robert G. Brown, extending earlier work by George
GNU GSL for the GSL random-number generators

SystemRequirements: DieHarder library (>= 2.8.1) from

developed by Robert G. Brown and David Bauer, extending earlier
SystemRequirements: DieHarder library (>= 3.31.1) from

Description: The RDieHarder packages provides an R interface to
the dieharder suite of random number generators and tests that
was developed by Robert G. Brown and David Bauer, extending
earlier work by George Marsaglia and others.

Title: R Interface to the 'DieHarder' RNG Test Suite

Description: The 'RDieHarder' package provides an R interface to
the 'DieHarder' suite of random number generators and tests that
earlier work by George Marsaglia and others. The 'DieHarder'
library is included, but if a version is already installed
SystemRequirements: GNU GSL for the GSL random-number generators

Author: Dirk Eddelbuettel, Robert G Brown, David Bauer

Title: Robust Test for Complete Independence in High-Dimensions
Description: Test Statistics for Independence in High-Dimensional Datasets. This package consists of two functions to perform the complete independence test based on test statistics proposed by Bulut (unpublished yet) and suggested by Najarzadeh (2021) <doi: 10.1080/03610926.2019.1702699>. The Bulut's statistic is not sensitive to outliers in high-dimensional data, unlike one of Najarzadeh (2021) <doi: 10.1080/03610926.2019.1702699>. So, the Bulut's statistic can be performed robustly by using RDnp function.