CMA: Synthesis of microarray-based classification

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>.
Date of publicationNone
MaintainerChristoph Bernau <bernau@ibe.med.uni-muenchen.de>
LicenseGPL (>= 2)
Version1.32.0

View on Bioconductor

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

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)

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-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-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-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-methods: Quadratic Discriminant Analysis

rfCMA: Classification based on Random Forests

rfCMA-methods: Classification based on Random Forests

roc: Receiver Operator Characteristic

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"

Files in this package

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

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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