Package: genMOSS
Title: Functions for the Bayesian Analysis of GWAS Data
Version: 1.2
Date: 2014-12-01
Author: Matthew Friedlander, Adrian Dobra, Helene Massam, and Laurent Briollais
Maintainer: Matthew Friedlander <friedlander.matthew@gmail.com>
Depends: R (>= 2.10), ROCR
Description: Implements the Mode Oriented Stochastic Search (MOSS) algorithm as well as a simple moving window approach to look for combinations of SNPs that are associated with a response.
License: GPL (>= 2)
LazyLoad: yes
Packaged: 2014-12-02 19:30:09 UTC; user
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-12-03 07:54:14
Package: PredictABEL
Title: Assessment of Risk Prediction Models
Version: 1.2-2
Date: 2014-12-20
Author: Suman Kundu, Yurii S. Aulchenko, A. Cecile J.W. Janssens
Maintainer: Suman Kundu <suman_math@yahoo.com>
Depends: R (>= 2.12.0), Hmisc, ROCR, epitools, PBSmodelling
Suggests: GenABEL
Description: PredictABEL includes functions to assess the performance of
risk models. The package contains functions for the various measures that are
used in empirical studies, including univariate and multivariate odds ratios
(OR) of the predictors, the c-statistic (or area under the receiver operating
characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of fit test,
reclassification table, net reclassification improvement (NRI) and
integrated discrimination improvement (IDI). Also included are functions
to create plots, such as risk distributions, ROC curves, calibration plot,
discrimination box plot and predictiveness curves. In addition to functions
to assess the performance of risk models, the package includes functions to
obtain weighted and unweighted risk scores as well as predicted risks using
logistic regression analysis. These logistic regression functions are
specifically written for models that include genetic variables, but they
can also be applied to models that are based on non-genetic risk factors only.
Finally, the package includes function to construct a simulated dataset with
genotypes, genetic risks, and disease status for a hypothetical population, which
is used for the evaluation of genetic risk models.
License: GPL (>= 2)
URL: http://www.genabel.org/packages/PredictABEL
Packaged: 2014-12-21 16:20:44 UTC; ripley
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-12-21 17:55:30
Package: cvAUC
Type: Package
Title: Cross-Validated Area Under the ROC Curve Confidence Intervals
Version: 1.1.0
Date: 2014-12-07
Author: Erin LeDell, Maya Petersen, Mark van der Laan
Maintainer: Erin LeDell <ledell@berkeley.edu>
Description: This package contains various tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators. The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively. One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods. The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package.
License: Apache License (== 2.0)
Depends: ROCR, data.table
URL: https://github.com/ledell/cvAUC
BugReports: https://github.com/ledell/cvAUC/issues
LazyLoad: yes
Packaged: 2014-12-09 01:09:45 UTC; ledell
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-12-09 07:12:38
Package: Comp2ROC
Title: Compare Two ROC Curves that Intersect
Version: 1.1.2
Date: 2016-05-18
Author: Ana C. Braga with contributions from Hugo Frade, Sara Carvalho and Andre M. Santiago
Maintainer: Ana C. Braga <acb@dps.uminho.pt>; Andre M. Santiago <andreportugalsantiago@gmail.com>
Description: Comparison of two ROC curves through the methodology proposed by Ana C. Braga.
License: GPL-2
Depends: R (>= 2.15.1), ROCR, boot
Packaged: 2016-05-18 22:58:58 UTC; andrew
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2016-05-21 00:03:36
Package: iFad
Type: Package
Title: An integrative factor analysis model for drug-pathway
association inference
Version: 3.0
Date: 2014-03-25
Author: Haisu Ma <haisu.ma.pku.2008@gmail.com>
Maintainer: Haisu Ma <haisu.ma.pku.2008@gmail.com>
Depends: R (>= 2.12.1), Rlab, MASS, coda, ROCR
Description: This package implements a Bayesian sparse factor model for the joint analysis of paired datasets, one is the gene expression dataset and the other is the drug sensitivity profiles, measured across the same panel of samples, e.g., cell lines. Prior knowledge about gene-pathway associations can be easily incorporated in the model to aid the inference of drug-pathway associations.
License: GPL (>= 2)
LazyLoad: yes
Packaged: 2014-03-27 22:56:40 UTC; Evelyn
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-03-27 23:58:34
Package: subtype
Type: Package
Title: Cluster analysis to find molecular subtypes and their assessment
Version: 1.0
Date: 2013-01-14
Author: Andrey Alexeyenko, Woojoo Lee and Yudi Pawitan
Maintainer: Woojoo Lee <lwj221@gmail.com>
Description: subtype performs a biclustering procedure on a input
dataset and assess whether resulting clusters are promising
subtypes. Note that the R-package rsmooth should be installed
before implementing subtype. rsmooth can be downloaded from
http://www.meb.ki.se/~yudpaw.
Depends: penalized, ROCR
License: GPL-2
Packaged: 2013-01-14 10:06:33 UTC; WOOJOO
Repository: CRAN
Date/Publication: 2013-01-14 13:53:58
Package: obliqueRF
Title: Oblique Random Forests from Recursive Linear Model Splits
Version: 0.3
Date: 2012-08-10
Depends: R (>= 2.0.0), stats, ROCR, pls, mda, e1071
Author: Bjoern Menze and Nico Splitthoff
Description: Random forest with oblique decision trees for binary
classification tasks. Discriminative node models in the tree
are based on: ridge regression, partial least squares
regression, logistic regression, linear support vector
machines, or random coefficients.
Maintainer: D.N. Splitthoff <nico.splitthoff@gmx.de>
License: GPL (>= 2)
Packaged: 2012-08-10 15:36:45 UTC; splitt
Repository: CRAN
Date/Publication: 2012-08-10 18:22:37
Package: rocc
Title: ROC based classification
Version: 1.2
Depends: ROCR
Author: Martin Lauss
Description: Functions for a classification method based on receiver
operating characteristics (ROC). Briefly, features are selected
according to their ranked AUC value in the training set. The
selected features are merged by the mean value to form a
metagene. The samples are ranked by their metagene value and
the metagene threshold that has the highest accuracy in
splitting the training samples is determined. A new sample is
classified by its metagene value relative to the threshold. In
the first place, the package is aimed at two class problems in
gene expression data, but might also apply to other problems.
Maintainer: Martin Lauss <martin.lauss@med.lu.se>
License: GPL (>= 2)
Packaged: 2010-10-04 08:58:00 UTC; Martin
Repository: CRAN
Date/Publication: 2010-10-04 10:03:36
providing 
.