Package: fastICA
Version: 1.1-8
Date: 2006-05-16
Title: FastICA algorithms to perform ICA and Projection Pursuit
Author:  J L Marchini <marchini@stats.ox.ac.uk>
C Heaton <chrisheaton99@yahoo.com> B D Ripley <ripley@stats.ox.ac.uk>
Maintainer: J L Marchini <marchini@stats.ox.ac.uk>
Depends: R (>= 2.3.0)
Description: Implementation of FastICA algorithm to perform
Independent Component Analysis (ICA) and Projection Pursuit.
License: GPL (version 2 or later)
URL: http://www.stats.ox.ac.uk/~marchini/software.html
Packaged: Tue May 16 09:38:57 2006; marchini

Package: fastICA
Version: 1.1-9
Date: 2007-10-10
Title: FastICA Algorithms to perform ICA and Projection Pursuit
Author: J L Marchini <marchini@stats.ox.ac.uk>
C Heaton <chrisheaton99@yahoo.com> B D Ripley <ripley@stats.ox.ac.uk>
Maintainer: Brian Ripley <ripley@stats.ox.ac.uk>
Depends: R (>= 2.3.0)
Description: Implementation of FastICA algorithm to perform
Independent Component Analysis (ICA) and Projection Pursuit.
License: GPL-2 | GPL-3
Packaged: Wed Oct 10 15:58:24 2007; ripley

Title: FastICA Algorithms to Perform ICA and Projection Pursuit

Author: J L Marchini, C Heaton and B D Ripley <ripley@stats.ox.ac.uk>

Title: Efficient Jonckheere-Terpstra Test Statistics for Robust Machine
Learning and Genome-Wide Association Studies
Author: Jiaxing Lin, Alexander Sibley, Ivo Shterev, and Kouros Owzar
Description: This 'Rcpp'-based package implements highly efficient functions for the calculation of the Jonckheere-Terpstra statistic. It can be used for a variety of applications, including feature selection in machine learning problems, or to conduct genome-wide association studies (GWAS) with multiple quantitative phenotypes. The code leverages 'OpenMP' directives for multi-core computing to reduce overall processing time.

Title: Efficient Jonckheere-Terpstra Test Statistics

Title: Fast Linear Step Up Procedure of Benjamini–Hochberg FDR Method
Description: An efficient algorithm to apply the Benjamini–Hochberg Linear Step Up FDR controlling procedure in huge-scale testing problems (proposed in Vered Madar and Sandra Batista(2016) <DOI:10.1093/bioinformatics/btw029>). Unlike "BH" method, the package does not require any p value ordering. Besides, it permits separating p values arbitrarily into computationally feasible chunks  of arbitrary size and produces the same results as those from applying linear step up BH procedure to the entire set of tests.