Description: Performs cryptographic randomness tests on a sequence of random
integers or bits. Included tests are greatest common divisor, birthday spacings,
book stack, adaptive chi-square, topological binary, and three random walk
tests. Tests except greatest common divisor and birthday spacings are not
covered by standard test suites. In addition to the chi-square goodness-of-fit
test, results of Anderson-Darling, Kolmogorov-Smirnov, and Jarque-Bera tests are
also generated by some of the cryptographic randomness tests.

tests (Ryabko and Monarev, 2005) <doi:10.1016/j.jspi.2004.02.010>. Tests except greatest common divisor and birthday spacings are not
Depends: kSamples, sfsmisc, Rmpfr, parallel
Packaged: 2022-04-22 06:57:42 UTC; haydardemirhan

Title: Identifying cryptic relatedness in genetic association studies
Author: Annick Joelle Nembot-Simo, Jinko Graham and Brad McNeney
Description: Exploratory tools to identify closely related subjects

Description: Exploratory tools to identify closely related subjects using autosomal genetic marker data.

Title: Identifying Cryptic Relatedness in Genetic Association Studies

Title: Testing for Change in C-Statistic
Description: Calculate the confidence interval and p value for change in C-statistic. The adjusted C-statistic is calculated by using formula as "Somers' Dxy rank correlation"/2+0.5. The confidence interval was calculated by using the bootstrap method. The p value was calculated by using the Z testing method. Please refer to the article of Peter Ganz et al. (2016) <doi:10.1001/jama.2016.5951>.
Imports: rms, survival, boot, stats, Hmisc

Title: MultiDimensional Feature Selection (MDFS)
person("Radosław", "Piliszek", email = "r.piliszek@uwb.edu.pl", role = c("aut", "cre")),
person("Andrzej", "Sułecki", email = "asulecki@gmail.com", role = "aut"),
person("Paweł", "Tabaszewski", email = "tabaszewski.pawel@gmail.com", role = "aut"),
person("Krzysztof", "Mnich", email = "k.mnich@uwb.edu.pl", role = "aut"),
person("Witold", "Rudnicki", email = "w.rudnicki@icm.edu.pl", role = "cph"))
Description: Functions for MultiDimensional Feature Selection (MDFS):
* calculating multidimensional information gains,
* finding interesting tuples for chosen variables,
CuCubes is also known as CUDA Cubes and it is a library that allows fast
CUDA-accelerated computation of information gains in binary classification
This package wraps CuCubes and provides an alternative CPU version as well
as helper functions for building MultiDimensional Feature Selectors.
Packaged: 2016-12-09 11:19:41 UTC; feature_selector