nlcv: Nested Loop Cross Validation

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>.

Version:	0.3.5
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
Published:	2018-06-29
DOI:	10.32614/CRAN.package.nlcv
Author:	Willem Talloen, Tobias Verbeke
Maintainer:	Laure Cougnaud <laure.cougnaud at openanalytics.eu>
License:	GPL-3
NeedsCompilation:	no
Materials:	NEWS
CRAN checks:	nlcv results

Documentation:

Reference manual:	nlcv.pdf
Vignettes:	nlcv

Downloads:

Package source:	nlcv_0.3.5.tar.gz
Windows binaries:	r-devel: nlcv_0.3.5.zip, r-release: nlcv_0.3.5.zip, r-oldrel: nlcv_0.3.5.zip
macOS binaries:	r-release (arm64): nlcv_0.3.5.tgz, r-oldrel (arm64): nlcv_0.3.5.tgz, r-release (x86_64): nlcv_0.3.5.tgz, r-oldrel (x86_64): nlcv_0.3.5.tgz
Old sources:	nlcv archive

Reverse dependencies:

Reverse suggests:

a4, a4Base

Linking:

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