Package: nlcv
Type: Package
Title: Nested Loop Cross Validation
Version: 0.3.4
Date: 2018-05-18
Author: Willem Talloen, Tobias Verbeke
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>
Description: Nested loop cross validation for classification purposes for misclassification error rate estimation.
The package supports several methodologies for feature selection: random forest, Student t-test, limma,
and provides an interface to the following classification methods in the 'MLInterfaces' package: linear,
quadratic discriminant analyses, random forest, bagging, prediction analysis for microarray, generalized
linear model, support vector machine (svm and ksvm). Visualizations to assess the quality of
the classifier are included: plot of the ranks of the features, scores plot for a specific
classification algorithm and number of features, misclassification rate
for the different number of features and classification algorithms tested and ROC plot.
For further details about the methodology, please check:
Markus Ruschhaupt, Wolfgang Huber, Annemarie Poustka, and Ulrich Mansmann (2004)
<doi:10.2202/1544-6115.1078>.
Depends: R (>= 2.10), a4Core, MLInterfaces (>= 1.22.0), xtable
Imports: limma, MASS, methods, graphics, Biobase, multtest,
RColorBrewer, pamr, randomForest, ROCR, ipred, e1071, kernlab
Suggests: RUnit, ALL
License: GPL-3
RoxygenNote: 6.0.1.9000
NeedsCompilation: no
Packaged: 2018-05-18 11:41:03 UTC; lcougnaud
Repository: CRAN
Date/Publication: 2018-05-18 12:01:52 UTC

Package: nlcv
Type: Package
Title: Nested Loop Cross Validation
Version: 0.3.5
Date: 2018-06-29
Author: Willem Talloen, Tobias Verbeke
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>
Description: Nested loop cross validation for classification purposes for misclassification error rate estimation.
The package supports several methodologies for feature selection: random forest, Student t-test, limma,
and provides an interface to the following classification methods in the 'MLInterfaces' package: linear,
quadratic discriminant analyses, random forest, bagging, prediction analysis for microarray, generalized
linear model, support vector machine (svm and ksvm). Visualizations to assess the quality of
the classifier are included: plot of the ranks of the features, scores plot for a specific
classification algorithm and number of features, misclassification rate
for the different number of features and classification algorithms tested and ROC plot.
For further details about the methodology, please check:
Markus Ruschhaupt, Wolfgang Huber, Annemarie Poustka, and Ulrich Mansmann (2004)
<doi:10.2202/1544-6115.1078>.
Depends: R (>= 2.10), a4Core, MLInterfaces (>= 1.22.0), xtable
Imports: limma, MASS, methods, graphics, Biobase, multtest,
RColorBrewer, pamr, randomForest, ROCR, ipred, e1071, kernlab
Suggests: RUnit, ALL
License: GPL-3
RoxygenNote: 6.0.1.9000
NeedsCompilation: no
Packaged: 2018-06-29 21:29:20 UTC; lcougnaud
Repository: CRAN
Date/Publication: 2018-06-29 21:49:59 UTC