Title: True Discovery Guarantee by the New Focus Level Procedure
Description: A new focus level procedure is developed based on the old focus level procedure of Goeman and Mansmann (2008) <doi:10.1093/bioinformatics/btm628> and the closed testing procedure with globaltest of Xu and Goeman (2020) <arXiv:2001.01541>. It produces the lower bound for the number of true discoveries in any gene set or GO (Gene Ontology) term. It is more powerful for the focus level GO terms chosen by user before seeing the data than the non-focus level GO terms or gene sets chosen after seeing the data.

Title: True Discovery Guarantee by Combining Partial Closed Testings
Description: Closed testing has been proved powerful for true discovery guarantee. The computation of closed testing is, however, quite burdensome. A general way to reduce computational complexity is to combine partial closed testings for some prespecified feature sets of interest. Partial closed testings are performed at Bonferroni-corrected alpha level to guarantee the lower bounds for the number of true discoveries in prespecified sets are simultaneously valid. For any post hoc chosen sets of interest, coherence property is used to get the lower bound. In this package, we implement closed testing with globaltest to calculate the lower bound for number of true discoveries, see Ningning Xu et.al (2021) <arXiv:2001.01541> for detailed description.

Title: Statistical Tests to Compare Curves with Recurrent Events
Author: Dr Carlos Miguel Martinez Manrique
Description: Implements the routines to compare the survival curves with recurrent events, including the estimations of survival curves. The first model is a  model for recurrent event, when the data are correlated or not  correlated. It was proposed by Wang and Chang (1999) <doi:10.2307/2669690>. In the independent case, the survival function can be  estimated by the generalization of the limit product model of Pena (2001) <doi:10.1198/016214501753381922>.

Package: newallelic
Version: 0.1
Date: 2006-05-19
Title: A fast, unbiased and exact allelic exact test
Author: Karl Forner <Karl.Forner@gmail.com>
Maintainer: Mickael Guedj <guedj@genopole.cnrs.fr>
Description: This is the implementation in R+C of a new association
test described in "A fast, unbiased and exact allelic exact test
for case-control association studies" (Submitted).
It appears that in most cases the classical chi-square test used
for testing for allelic association on genotype data is biased.
Our test is unbiased, exact but fast throught careful optimization.
License: GPL version 2 or newer
Packaged: Thu May 18 10:14:51 2006; hornik