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 (Ryabko and Monarev, 2005) <doi:10.1016/j.jspi.2004.02.010>. 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.

Identifying cryptic relatedness in genetic association studies
Annick Joelle Nembot-Simo, Jinko Graham and Brad McNeney
Exploratory tools to identify closely related subjects
using autosomal genetic marker data.

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

Identifying Cryptic Relatedness in Genetic Association Studies

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>.

c(
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"))
Functions for MultiDimensional Feature Selection (MDFS):
* calculating multidimensional information gains,
* finding interesting tuples for chosen variables,
* scoring variables,
* finding important variables,
* plotting selection results.
CuCubes is also known as CUDA Cubes and it is a library that allows fast
CUDA-accelerated computation of information gains in binary classification
problems.
This package wraps CuCubes and provides an alternative CPU version as well
as helper functions for building MultiDimensional Feature Selectors.
Radosław Piliszek [aut, cre],
Andrzej Sułecki [aut],
Paweł Tabaszewski [aut],
Krzysztof Mnich [aut],
Witold Rudnicki [cph]