Package: netClass
Version: 1.2.1
Date: 2013-12-03
Title: netClass: An R Package for Network-Based Biomarker Discovery
Author: Yupeng Cun
Maintainer: Yupeng Cun <yupeng.cun@gmail.com>
Description: netClass is an R package for network-based feature (gene)
selection for biomarkers discovery via integrating biological
information. This package adapts the following 5 algorithms
for classifying and predicting gene expression data using prior
knowledge: 1) average gene expression of pathway (aep); 2)
pathway activities classification (PAC); 3) Hub network
Classification (hubc); 4) filter via top ranked genes (FrSVM);
5) network smoothed t-statistic (stSVM).
Depends: R (>= 2.14), kernlab
Imports: AnnotationDbi, Matrix, ROCR, graph, igraph, samr
Suggests: parallel, Biobase, KEGG.db, pathClass
License: GPL (>= 2)
LazyLoad: yes
Packaged: 2013-12-03 21:27:02 UTC; cun
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-12-03 22:44:46
Package: kappalab
Version: 0.4-7
Date: 2015-07-18
Title: Non-Additive Measure and Integral Manipulation Functions
Author: Michel Grabisch, Ivan Kojadinovic, Patrick Meyer.
Maintainer: Ivan Kojadinovic <ivan.kojadinovic@univ-pau.fr>
Description: S4 tool box for capacity (or non-additive measure, fuzzy measure) and integral manipulation in a finite setting. It contains routines for handling various types of set functions such as games or capacities. It can be used to compute several non-additive integrals: the Choquet integral, the Sugeno integral, and the symmetric and asymmetric Choquet integrals. An analysis of capacities in terms of decision behavior can be performed through the computation of various indices such as the Shapley value, the interaction index, the orness degree, etc. The well-known Möbius transform, as well as other equivalent representations of set functions can also be computed. Kappalab further contains seven capacity identification routines: three least squares based approaches, a method based on linear programming, a maximum entropy like method based on variance minimization, a minimum distance approach and an unsupervised approach based on parametric entropies. The functions contained in Kappalab can for instance be used in the framework of multicriteria decision making or cooperative game theory.
Depends: R (>= 2.1.0), methods, lpSolve, quadprog, kernlab
Encoding: latin1
License: CeCILL
NeedsCompilation: yes
Packaged: 2015-07-18 10:54:47 UTC; ikojadin
Repository: CRAN
Date/Publication: 2015-07-18 17:00:04
Package: SVMMaj
Type: Package
Title: SVMMaj algorithm
Version: 0.2-2
Date: 2010-12-16
Author: Hoksan Yip, Patrick J.F. Groenen, Georgi Nalbantov
Maintainer: Hok San Yip <hoksan@gmail.com>
Description: Implements the SVM-Maj algorithm to train data with
Support Vector Machine, this algorithm uses two efficient
updates, one for linear kernel and one for the nonlinear
kernel.
Imports: graphics, stats, MASS, methods
Depends: R (>= 2.9.0), kernlab
License: GPL-2
LazyData: Yes
Packaged: 2011-01-13 07:33:21 UTC; Hoksan
Repository: CRAN
Date/Publication: 2011-01-13 12:09:49
Package: CVST
Type: Package
Title: Fast Cross-Validation via Sequential Testing
Version: 0.2-1
Date: 2013-12-10
Depends: kernlab, Matrix
Author: Tammo Krueger, Mikio Braun
Maintainer: Tammo Krueger <tammokrueger@googlemail.com>
Description: This package implements the fast cross-validation via sequential testing (CVST) procedure. CVST is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating underperforming candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.
License: GPL (>= 2.0)
Packaged: 2013-12-10 13:06:36 UTC; tammok
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-12-10 14:50:04
Package: svmadmm
Title: Linear/Nonlinear SVM Classification Solver Based on ADMM and
IADMM Algorithms
Version: 0.3
Author: Ben DAI <bendai2-c@my.cityu.edu.hk>; Junhui Wang <j.h.wang@cityu.edu.hk>
Maintainer: Ben DAI <bendai2-c@my.cityu.edu.hk>
Description:
Solve large-scale regularised linear/kernel classification by using ADMM and IADMM algorithms. This package provides linear L2-regularised primal classification (both ADMM and IADMM are available), kernel L2-regularised dual classification (IADMM) as well as L1-regularised primal classification (both ADMM and IADMM are available).
Depends: R (>= 3.2.2), kernlab
License: GPL-2
LazyData: true
Repository: CRAN
NeedsCompilation: yes
Packaged: 2016-03-29 05:08:45 UTC; ben
Date/Publication: 2016-03-29 10:50:29
Package: probsvm
Type: Package
Title: probsvm: Class probability estimation for Support Vector
Machines
Date: 2013-5-16
Version: 1.00
Author: Chong Zhang, Seung Jun Shin, Junhui Wang, Yichao Wu, Hao Helen
Zhang, and Yufeng Liu
Maintainer: Chong Zhang <chongz@live.unc.edu>
Depends: kernlab
Description: This package provides multiclass conditional probability
estimation for the SVM, which is distributional assumption
free.
License: GPL-2
LazyLoad: yes
Packaged: 2013-05-18 23:48:46 UTC; NC
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-05-19 08:15:01
Package: pathClass
Type: Package
Title: Classification using biological pathways as prior knowledge
Version: 0.9.4
Date: 2013-06-25
Author: Marc Johannes
Maintainer: Marc Johannes <JohannesMarc@gmail.com>
Description: pathClass is a collection of classification methods that
use information about feature connectivity in a biological
network as an additional source of information. This additional
knowledge is incorporated into the classification a priori.
Several authors have shown that this approach significantly
increases the classification performance.
Depends: R (>= 2.14), svmpath, kernlab, affy, Biobase, ROCR, igraph, lpSolve
Enhances: parallel
Suggests: hu6800.db, golubEsets
License: GPL (>= 2)
LazyLoad: yes
Collate: 'CrossValidation.R' 'GeneRank.R' 'GraphSVM.R'
'networkBasedSVM.R' 'PathwayMethods.R'
'RecursiveFeatureElimination.R' 'RRFE.R' 'SpanBound.R' 'SVMs.R'
Packaged: 2013-06-29 11:48:33 UTC; mj
Repository: CRAN
Date/Publication: 2013-07-01 07:43:40
NeedsCompilation: no
providing 
.