Package: MSeasy
Type: Package
Title: Preprocessing of Gas Chromatography-Mass Spectrometry (GC-MS)
data
Version: 5.3.3
Date: 2013-03-18
Depends: amap, clValid, cluster, fpc, mzR, xcms
Suggests: tcltk
Author: Elodie Courtois <courtoiselodie@gmail.com>, Yann Guitton
<yann.guitton@gmail.com>, Florence Nicole
<florence.nicole@univ-st-etienne.fr>
Maintainer: Yann GUITTON <yann.guitton@gmail.com>
Description: Package for the detection of molecules in complex mixtures
of compounds. It creates an initial_DATA matrix from several
GC-MS analyses by collecting and assembling the information
from chromatograms and mass spectra (MS.DataCreation), It can
read several format (ASCII, CDF, mzML, mzXML or mzData).It
tests for the best unsupervised clustering method to group
similar mass spectra into molecules (MS.test.clust).It runs the
optimal unsupervised clustering method on the initial_DATA
matrix, identifies the optimal number of clusters, produces
different files for facilitating the quality control and
identification of putative molecules, and returns
fingerprinting or profiling matrices (MS.clust).It converts
output files from MS.clust for NIST mass spectral library
search and ARISTO webtool search
License: GPL-2
URL: http://sites.google.com/site/rpackagemseasy/
LazyData: yes
Packaged: 2013-03-19 16:52:04 UTC; chimimar
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-03-20 17:07:31
Package: COMMUNAL
Type: Package
Title: Robust Selection of Cluster Number K
Version: 1.1.0
Date: 2015-10-12
Author: Albert Chen [aut, cre], Timothy E Sweeney [aut], Olivier Gevaert [ths]
Maintainer: Albert Chen <acc2015@stanford.edu>
Authors@R: c(person("Albert", "Chen", role = c("aut", "cre"),
email = "acc2015@stanford.edu"),
person(c("Timothy", "E"), "Sweeney", role = "aut",
email = "tes17@stanford.edu"),
person("Olivier", "Gevaert", role = "ths",
email = "ogevaert@stanford.edu"))
Depends: R (>= 2.10), cluster, clValid, fpc
Suggests: RUnit, NMF, ConsensusClusterPlus, rgl, kohonen
Imports: methods, grDevices, graphics, stats, utils
Description: Facilitates optimal clustering of a data set. Provides a framework to run a wide range of clustering algorithms to determine the optimal number (k) of clusters in the data. Then analyzes the cluster assignments from each clustering algorithm to identify samples that repeatedly classify to the same group. We call these 'core clusters', providing a basis for later class discovery.
NeedsCompilation: no
License: GPL-2
Packaged: 2015-10-11 21:02:44 UTC; Albert
Repository: CRAN
Date/Publication: 2015-10-12 00:26:16
providing 
.