Commit 47b5cb38 authored by Ghislain Durif's avatar Ghislain Durif
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Write proper README file

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This file explains the repository structure of your project. A more
detailed guide to R-Forge is available by
Theußl and Zeileis (2010) [1] and the R-Forge Administration and
Development Team (2009) [2].
1. Introduction
R is free software distributed under a GNU-style copyleft. R-Forge is
a central platform for the development of R packages, R-related
software and further projects. Among many other web-based features it
provides facilities for collaborative source code management via
Subversion (SVN) [3].
2. The directory you're in
This is the repository of your project. It contains two important
pre-defined directories namely 'pkg' and 'www'. These directories must
not be deleted otherwise R-Forge's core functionality will not be
available (i.e., daily checking and building of your package or the
project websites).
'pkg' and 'www' are standardized and therefore are going to be
described in this README. The rest of your repository can be used as
you like.
3. 'pkg' directory
To make use of the package building and checking feature the package
source code has to be put into the 'pkg' directory of your repository
(i.e., 'pkg/DESCRIPTION', 'pkg/R', 'pkg/man', etc.) or, alternatively,
a subdirectory of 'pkg'. The latter structure allows for having more
than one package in a single project, e.g., if a project consists of
the packages foo and bar then the source code will be located in
'pkg/foo' and 'pkg/bar', respectively.
R-Forge automatically examines the 'pkg' directory of every repository
and builds the package sources as well as the package binaries on a
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are provided in the 'R Packages' tab for download or can be installed
directly in R from a CRAN-style repository using
'install.packages("foo", repos="")'.
Furthermore, in the 'R Packages' tab developers can examine logs
generated on different platforms by the build and check process.
4. 'www' directory
Developers may present their project on a subdomain of R-Forge, e.g.,
'', or via a link to an external
This directory contains the project homepage which gets updated hourly
on R-Forge, so please take into consideration that it will not be
available right after you commit your changes or additions.
5. Help
If you need help don't hesitate to submit a support request at,
search the forum,
or contact us at
6. References
[1] Stefan Theußl and Achim Zeileis. Collaborative software development
using R-Forge. The R Journal, 1(1):9-14, May 2009. URL
[2] R-Forge Administration and Development Team. RForge User’s Manual,
2008. URL
[3] C. M. Pilato, B. Collins-Sussman, and B. W. Fitzpatrick. Version
Control with Subversion. O’Reilly, 2004. Full book available online at
# R-package plsgenomics
PLS Analyses for Genomics
## Author
Anne-Laure Boulesteix <>, Ghislain Durif <>, Sophie Lambert-Lacroix <>, Julie Peyre <>, and Korbinian Strimmer <>.
Maintainer: Ghislain Durif <>
## Description
Routines for PLS-based genomic analyses, implementing PLS methods for classification with microarray data and prediction of transcription factor activities from combined ChIP-chip analysis. The >=1.2-1 versions include two new classification methods for microarray data: GSIM and Ridge PLS. The >=1.3 versions includes a new classification method combining variable selection and compression in logistic regression context: logit-SPLS; and an adaptive version of the sparse PLS.
## Installation
You can install the `plsgenomics` R package with the following R commands:
devtools::install_git("", subdir="pkg")
To install the `devtools` package, you can run:
You can also use the git repository available at <>,
then build and install the package with Rstudio (the [project file](./plsgenomics.Rproj)
is set accordingly) or with the R command line tools.
Once you cloned the git repository, you can also run:
devtools::install("/path/to/plsgenomics/pkg") # you should edit the path
## Licence
The `plsgenomics` package is distributed under the GPL (>=2) licence.
## Example
Examples regarding the sparse PLS method and the sparse PLS approach for logistic regression developped in Durif et al. (2018) can be respectively found in these two scripts: [spls_example.R](./spls_example.R) and [logit_spls_example.R](./logit_spls_example.R).
## Reference
Durif, G., Modolo, L., Michaelsson, J., Mold, J.E., Lambert-Lacroix, S., Picard, F., 2018. High dimensional classification with combined adaptive sparse PLS and logistic regression. Bioinformatics 34, 485–493.
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