Estimates hierarchical F-statistics from haploid or
diploid genetic data with any numbers of levels in the hierarchy, following the
algorithm of Yang (Evolution(1998), 52:950).
Tests via randomisations the significance
of each F and variance components, using the likelihood-ratio statistics G
(Goudet et al. (1996) <https://academic.oup.com/genetics/article/144/4/1933/6017091>).
Estimates genetic diversity statistics
for haploid and diploid genetic datasets in various formats, including inbreeding and
coancestry coefficients, and population specific F-statistics following
Weir and Goudet (2017) <https://academic.oup.com/genetics/article/206/4/2085/6072590>.

Convex Hierarchical Testing of Interactions
Jacob Bien, Noah Simon, and Rob Tibshirani
Implementation of the convex hierarchical testing (CHT) procedure
introduced in Bien, Simon, and Tibshirani (2015) Convex Hierarchical Testing
of Interactions. Annals of Applied Statistics. Vol. 9, No. 1, 27-42.

Two-Sample Tests for Equality of Means in High Dimension
Performs the generalized component test from Gregory et al (2015), as well as the tests from Chen and Qin (2010), Srivastava and Kubokawa (2013), and Cai, Liu, and Xia (2014) for equality of two population mean vectors when the length of the vectors exceeds the sample size.

Testing Two-Sample Mean in High Dimension
Implements the high-dimensional two-sample test
proposed by Zhang (2019) <http://hdl.handle.net/2097/40235>.
It also implements the test proposed by Srivastava, Katayama,
and Kano (2013) <doi:10.1016/j.jmva.2012.08.014>. These tests
are particularly suitable to high dimensional data from two populations
for which the classical multivariate Hotelling's T-square test fails due
to sample sizes smaller than dimensionality. In this case, the ZWL and ZWLm
tests proposed by Zhang (2019) <http://hdl.handle.net/2097/40235>,
referred to as zwl_test() in this package, provide a reliable and powerful test.

Feature Selection for High Dimensional Survival Data
mlr3, mlr3proba, mlr3learners, survival, gtools, tibble,
dplyr, utils, coxme, missForest
c(person(("Atanu"), "Bhattacharjee",
email="atanustat@gmail.com",
role=c("aut", "cre","ctb")),
person(("Gajendra"), "K. Vishwakarma", role=c("aut","ctb")),
person(("Souvik"),"Banerjee", role=c("aut","ctb")))
Perform high dimensional Feature Selection in the presence of survival outcome.
Based on Feature Selection method and different survival analysis, it will obtain the best
markers with optimal threshold levels according to their effect on disease progression
and produce the most consistent level according to those threshold values.
The functions' methodology is based on by Sonabend et al (2021) <doi:10.1093/bioinformatics/btab039>  and Bhattacharjee  et al (2021) <arXiv:2012.02102>.
Atanu Bhattacharjee [aut, cre, ctb],
Gajendra K. Vishwakarma [aut, ctb],
Souvik Banerjee [aut, ctb]