R Graphical Manual

Last data update: 2014.03.03

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Results 1 - 4 of 4 found.
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R2GUESS : Wrapper Functions for GUESS

Package: R2GUESS
Version: 1.7
Date: 2016-01-26
Title: Wrapper Functions for GUESS
Authors@R: c(person("Gianluca", "Campanella",
role=c("cre"), email="gianluca@campanella.org"),
person("Benoit", "Liquet",
role="aut", email="b.liquet@uq.edu.au"),
person("Marc", "Chadeau-Hyam",
role="aut", email="m.chadeau@imperial.ac.uk"),
person("Leonardo", "Bottolo",
role="ctb", email="l.bottolo@imperial.ac.uk"),
person("Sylvia", "Richardson",
role="ctb", email="sylvia.richardson@mrc-bsu.cam.ac.uk"))
Depends: R (>= 3.0.0), fields, MCMCpack, methods, mixOmics, mvtnorm,
snowfall
Description: Wrapper functions for GUESS, a GPU-enabled sparse Bayesian variable
selection method for linear regression based analysis of possibly
multivariate/correlated outcomes.
License: GPL (>= 2)
SystemRequirements: GNU Scientific Library (>= 1.12)
NeedsCompilation: yes
Packaged: 2016-01-26 13:14:38 UTC; gc
Author: Gianluca Campanella [cre],
Benoit Liquet [aut],
Marc Chadeau-Hyam [aut],
Leonardo Bottolo [ctb],
Sylvia Richardson [ctb]
Maintainer: Gianluca Campanella <gianluca@campanella.org>
Repository: CRAN
Date/Publication: 2016-01-26 15:19:28

● Data Source: CranContrib
● 14 images, 27 functions, 3 datasets
● Reverse Depends: 0

YuGene : A Simple Approach to Scale Gene Expression Data Derived from Different Platforms for Integrated Analyses

Package: YuGene
Type: Package
Title: A Simple Approach to Scale Gene Expression Data Derived from
Different Platforms for Integrated Analyses
Version: 1.1.5
Date: 2015-11-19
Author: Kim-Anh Le Cao, Florian Rohart, Leo McHugh, Othmar Korn, Christine A. Wells
Maintainer: Florian Rohart <f.rohart@uq.edu.au>
Description: Simple method for comparison of gene
expression generated across different experiments, and on
different platforms; that does not require global
renormalization, and is not restricted to comparison of
identical probes. YuGene works on a range of microarray dataset
distributions, such as between manufacturers. The resulting
output allows direct comparisons of gene expression between
experiments and experimental platforms.
License: GPL (>= 2)
Packaged: 2015-11-19 04:22:12 UTC; florian
Depends: mixOmics , R (>= 2.10)
Repository: CRAN
Date/Publication: 2015-11-19 11:02:18
NeedsCompilation: no

● Data Source: CranContrib
● 0 images, 4 functions, 2 datasets
● Reverse Depends: 0

bootsPLS : Bootstrap Subsamplings of Sparse Partial Least Squares - Discriminant Analysis for Classification and Signature Identification

Package: bootsPLS
Type: Package
Title: Bootstrap Subsamplings of Sparse Partial Least Squares -
Discriminant Analysis for Classification and Signature
Identification
Version: 1.0.3
Author: Florian Rohart [aut, cre], Kim-Anh Le Cao [boss], Christine Wells [boss]
Maintainer: Florian Rohart <florian.rohart@gmail.com>
Description: Applicable to any classification problem with more than 2 classes. It relies on bootstrap subsamplings of sPLS-DA and provides tools to select the most stable variables (defined as the ones consistently selected over the bootstrap subsamplings) and to predict the class of test samples.
License: GPL-3
Depends: mixOmics
Packaged: 2015-08-20 05:55:44 UTC; florian
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-08-20 10:39:44

● Data Source: CranContrib
● 0 images, 13 functions, 0 datasets
● Reverse Depends: 0

sgPLS : Sparse Group Partial Least Square Methods

Package: sgPLS
Type: Package
Title: Sparse Group Partial Least Square Methods
Version: 1.4
Date: 2015-11-25
Author: Benoit Liquet and Pierre Lafaye de Micheaux
Maintainer: Benoit Liquet <b.liquet@uq.edu.au>
Depends: mixOmics
Description: The Sparse Group Partial Least Square package (sgPLS) provides sparse, group, and sparse group versions of partial least square regression models.
License: GPL (>= 2.0)
NeedsCompilation: no
Packaged: 2015-11-25 09:23:19 UTC; liquetwe
Repository: CRAN
Date/Publication: 2015-11-25 13:49:15

● Data Source: CranContrib
● 0 images, 14 functions, 0 datasets
● Reverse Depends: 0