CMA: Synthesis of microarray-based classification
Version 1.34.0

This package provides a comprehensive collection of various microarray-based classification algorithms both from Machine Learning and Statistics. Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed combined or stepwise in a user-friendly environment.

AuthorMartin Slawski <ms@cs.uni-sb.de>, Anne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>, Christoph Bernau <bernau@ibe.med.uni-muenchen.de>.
Bioconductor views Classification DecisionTree
Date of publicationNone
MaintainerChristoph Bernau <bernau@ibe.med.uni-muenchen.de>
LicenseGPL (>= 2)
Version1.34.0
Package repositoryView on Bioconductor
InstallationInstall the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("CMA")

Getting started

Package overview

Popular man pages

golub: ALL/AML dataset of Golub et al. (1999)
internals: Internal functions
LassoCMA: L1 penalized logistic regression
nnetCMA: Feed-forward Neural Networks
pls_rfCMA: Partial Least Squares followed by random forests
roc: Receiver Operator Characteristic
tune-methods: Hyperparameter tuning for classifiers
See all...

All man pages Function index File listing

Man pages

best: Show best hyperparameter settings
boxplot: Make a boxplot of the classifier evaluation
classification: General method for classification with various methods
classification-methods: General method for classification with various methods
cloutput-class: "cloutput"
clvarseloutput-class: "clvarseloutput"
CMA-package: Synthesis of microarray-based classification
compare: Compare different classifiers
compare-methods: Compare different classifiers
compBoostCMA: Componentwise Boosting
compBoostCMA-methods: Componentwise Boosting
dldaCMA: Diagonal Discriminant Analysis
dldaCMA-methods: Diagonal Discriminant Analysis
ElasticNetCMA: Classfication and variable selection by the ElasticNet
ElasticNetCMA-methods: Classfication and variable selection by the ElasticNet
evaloutput-class: "evaloutput"
evaluation: Evaluation of classifiers
evaluation-methods: Evaluation of classifiers
fdaCMA: Fisher's Linear Discriminant Analysis
fdaCMA-methods: Fisher's Linear Discriminant Analysis
filter: Filter functions for Gene Selection
flexdaCMA: Flexible Discriminant Analysis
flexdaCMA-methods: Flexible Discriminant Analysis
ftable: Cross-tabulation of predicted and true class labels
gbmCMA: Tree-based Gradient Boosting
gbmCMA-methods: Tree-based Gradient Boosting
GenerateLearningsets: Repeated Divisions into learn- and tets sets
genesel-class: "genesel"
GeneSelection: General method for variable selection with various methods
GeneSelection-methods: General method for variable selection with various methods
golub: ALL/AML dataset of Golub et al. (1999)
internals: Internal functions
join: Combine list elements returned by the method classification
join-methods: Combine list elements returned by the method classification
khan: Small blue round cell tumor dataset of Khan et al. (2001)
knnCMA: Nearest Neighbours
knnCMA-methods: Nearest Neighbours
LassoCMA: L1 penalized logistic regression
LassoCMA-methods: L1 penalized logistic regression
ldaCMA: Linear Discriminant Analysis
ldaCMA-methods: Linear Discriminant Analysis
learningsets-class: "learningsets"
nnetCMA: Feed-forward Neural Networks
nnetCMA-methods: Feed-Forward Neural Networks
obsinfo: Classifiability of observations
pknnCMA: Probabilistic Nearest Neighbours
pknnCMA-methods: Probabilistic nearest neighbours
Planarplot: Visualize Separability of different classes
Planarplot-methods: Visualize Separability of different classes
plot,cloutput-method: Probability plot
plot,genesel-method: Barplot of variable importance
plot,tuningresult-method: Visualize results of tuning
plrCMA: L2 penalized logistic regression
plrCMA-methods: L2 penalized logistic regression
pls_ldaCMA: Partial Least Squares combined with Linear Discriminant...
pls_ldaCMA-methods: Partial Least Squares combined with Linear Discriminant...
pls_lrCMA: Partial Least Squares followed by logistic regression
pls_lrCMA-methods: Partial Least Squares followed by logistic regression
pls_rfCMA: Partial Least Squares followed by random forests
pls_rfCMA-methods: Partial Least Squares followed by random forests
pnnCMA: Probabilistic Neural Networks
pnnCMA-methods: Probabilistic Neural Networks
prediction: General method for predicting classes of new observations
prediction-methods: General method for predicting class lables of new...
predoutput-class: "predoutput"
qdaCMA: Quadratic Discriminant Analysis
qdaCMA-methods: Quadratic Discriminant Analysis
rfCMA: Classification based on Random Forests
rfCMA-methods: Classification based on Random Forests
roc: Receiver Operator Characteristic
scdaCMA: Shrunken Centroids Discriminant Analysis
scdaCMA-methods: Shrunken Centroids Discriminant Analysis
shrinkldaCMA: Shrinkage linear discriminant analysis
shrinkldaCMA-methods: Shrinkage linear discriminant analysis
summary: Summarize classifier evaluation
svmCMA: Support Vector Machine
svmCMA-methods: Support Vector Machine
toplist: Display 'top' variables
tune: Hyperparameter tuning for classifiers
tune-methods: Hyperparameter tuning for classifiers
tuningresult-class: "tuningresult"
varseloutput-class: "varseloutput"
weighted_mcr: Tuning / Selection bias correction
weighted_mcr-methods: General method for tuning / selection bias correction
wmc: Tuning / Selection bias correction based on matrix of...
wmc-methods: General method for tuning / selection bias correction based...
wmcr_result-class: "wmcr.result"

Functions

Files

DESCRIPTION
NAMESPACE
R
R/ElasticNetCMA.r
R/GeneSelection.r
R/GenerateLearningsets.r
R/LassoCMA.r
R/Planarplot.r
R/classes.r
R/classification.r
R/compBoostCMA.r
R/compare.r
R/dldaCMA.r
R/evaluation.r
R/fdaCMA.r
R/filter.r
R/flexdaCMA.r
R/gbmCMA.r
R/internals.r
R/join.r
R/knnCMA.r
R/ldaCMA.r
R/nnetCMA.r
R/pknnCMA.r
R/plrCMA.r
R/pls_ldaCMA.r
R/pls_lrCMA.r
R/pls_rfCMA.r
R/pnnCMA.r
R/qdaCMA.r
R/rfCMA.r
R/scdaCMA.r
R/shrinkldaCMA.r
R/svmCMA.r
R/tune.r
R/weighted_mcr.r
R/wmc.r
build
build/vignette.rds
data
data/golub.rda
data/khan.rda
inst
inst/doc
inst/doc/CMA_vignette.R
inst/doc/CMA_vignette.pdf
inst/doc/CMA_vignette.rnw
man
man/CMA-package.Rd
man/ElasticNetCMA-methods.Rd
man/ElasticNetCMA.Rd
man/GeneSelection-methods.Rd
man/GeneSelection.Rd
man/GenerateLearningsets.Rd
man/LassoCMA-methods.Rd
man/LassoCMA.Rd
man/Planarplot-methods.Rd
man/Planarplot.Rd
man/best.Rd
man/boxplot.Rd
man/classification-methods.Rd
man/classification.Rd
man/cloutput-class.Rd
man/clvarseloutput-class.Rd
man/compBoostCMA-methods.Rd
man/compBoostCMA.Rd
man/compare-methods.Rd
man/compare.Rd
man/dldaCMA-methods.Rd
man/dldaCMA.Rd
man/evaloutput-class.Rd
man/evaluation-methods.Rd
man/evaluation.Rd
man/fdaCMA-methods.Rd
man/fdaCMA.Rd
man/filter.rd
man/flexdaCMA-methods.Rd
man/flexdaCMA.Rd
man/ftable.Rd
man/gbmCMA-methods.Rd
man/gbmCMA.Rd
man/genesel-class.Rd
man/golub.Rd
man/internals.rd
man/join-methods.Rd
man/join.Rd
man/khan.Rd
man/knnCMA-methods.Rd
man/knnCMA.Rd
man/ldaCMA-methods.Rd
man/ldaCMA.rd
man/learningsets-class.Rd
man/nnetCMA-methods.Rd
man/nnetCMA.Rd
man/obsinfo.Rd
man/pknnCMA-methods.Rd
man/pknnCMA.Rd
man/plot,cloutput-method.Rd
man/plot,genesel-method.Rd
man/plot,tuningresult-method.Rd
man/plrCMA-methods.Rd
man/plrCMA.Rd
man/pls_ldaCMA-methods.Rd
man/pls_ldaCMA.Rd
man/pls_lrCMA-methods.Rd
man/pls_lrCMA.rd
man/pls_rfCMA-methods.Rd
man/pls_rfCMA.Rd
man/pnnCMA-methods.Rd
man/pnnCMA.rd
man/prediction-methods.Rd
man/prediction.Rd
man/predoutput-class.Rd
man/qdaCMA-methods.Rd
man/qdaCMA.rd
man/rfCMA-methods.Rd
man/rfCMA.Rd
man/roc.Rd
man/scdaCMA-methods.Rd
man/scdaCMA.rd
man/shrinkldaCMA-methods.Rd
man/shrinkldaCMA.Rd
man/summary.Rd
man/svmCMA-methods.Rd
man/svmCMA.Rd
man/toplist.Rd
man/tune-methods.Rd
man/tune.Rd
man/tuningresult-class.Rd
man/varseloutput-class.Rd
man/weighted_mcr-methods.Rd
man/weighted_mcr.Rd
man/wmc-methods.Rd
man/wmc.Rd
man/wmcr_result-class.Rd
vignettes
vignettes/CMA_vignette.rnw
vignettes/classification.bib
vignettes/preamble.tex
CMA documentation built on May 20, 2017, 9:16 p.m.

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