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Variable Selection,\nHyperparameter tuning, Evaluation and Comparison can be\nperformed combined or stepwise in a user-friendly environment.", "Collate": "classes.r GenerateLearningsets.r GeneSelection.r tune.r\nclassification.r evaluation.r join.r compare.r Planarplot.r\ncompBoostCMA.r dldaCMA.r ElasticNetCMA.r fdaCMA.r flexdaCMA.r\ngbmCMA.r knnCMA.r LassoCMA.r ldaCMA.r nnetCMA.r pknnCMA.r\nplrCMA.r pls_ldaCMA.r pls_lrCMA.r pls_rfCMA.r pnnCMA.r qdaCMA.r\nrfCMA.r scdaCMA.r weighted_mcr.r wmc.r shrinkldaCMA.r svmCMA.r\nfilter.r internals.r", "License": "GPL (>= 2)", "biocViews": "Classification, DecisionTree", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-04-28 12:32:58 UTC", "RemoteUrl": "https://github.com/bioc/CMA", "RemoteRef": "HEAD", "RemoteSha": "b83e472a68879213b1b7ecdb5d28f1c9c0635993", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-07 09:36:09 UTC", "User": "root" }, "_user": "bioc", "_type": "src", "_file": "CMA_1.71.0.tar.gz", "_fileid": 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"title": "Flexible Discriminant Analysis", "topics": [ "flexdaCMA,data.frame,missing,formula-method", "flexdaCMA,ExpressionSet,character,missing-method", "flexdaCMA,matrix,factor,missing-method", "flexdaCMA,matrix,numeric,missing-method", "flexdaCMA-methods" ] }, { "page": "ftable", "title": "Cross-tabulation of predicted and true class labels", "topics": [ "ftable,cloutput-method" ] }, { "page": "gbmCMA", "title": "Tree-based Gradient Boosting", "topics": [ "gbmCMA" ] }, { "page": "gbmCMA-methods", "title": "Tree-based Gradient Boosting", "topics": [ "gbmCMA,data.frame,missing,formula-method", "gbmCMA,ExpressionSet,character,missing-method", "gbmCMA,matrix,factor,missing-method", "gbmCMA,matrix,numeric,missing-method", "gbmCMA-methods" ] }, { "page": "GenerateLearningsets", "title": "Repeated Divisions into learn- and tets sets", "topics": [ "GenerateLearningsets" ] }, { "page": "genesel-class", "title": "\"genesel\"", "topics": [ "genesel", "genesel-class", "show,genesel-method" ] }, { "page": "GeneSelection", "title": "General method for variable selection with various methods", "topics": [ "GeneSelection" ] }, { "page": "GeneSelection-methods", "title": "General method for variable selection with various methods", "topics": [ "GeneSelection,data.frame,missing,formula-method", "GeneSelection,ExpressionSet,character,missing-method", "GeneSelection,matrix,factor,missing-method", "GeneSelection,matrix,numeric,missing-method", "GeneSelection-methods" ] }, { "page": "golub", "title": "ALL/AML dataset of Golub et al. (1999)", "topics": [ "golub" ] }, { "page": "internals", "title": "Internal functions", "topics": [ "bklr", "bklr.predict", "bkreg", "care.dev", "care.exp", "characterplot", "mklr", "mklr.predict", "mkreg", "my.care.exp", "plotprob", "ROCinternal", "roundvector", "rowswaps", "safeexp" ] }, { "page": "join", "title": "Combine list elements returned by the method classification", "topics": [ "join" ] }, { "page": "join-methods", "title": "Combine list elements returned by the method classification", "topics": [ "join,list-method", "join-methods" ] }, { "page": "khan", "title": "Small blue round cell tumor dataset of Khan et al. (2001)", "topics": [ "khan" ] }, { "page": "knnCMA", "title": "Nearest Neighbours", "topics": [ "knnCMA" ] }, { "page": "knnCMA-methods", "title": "Nearest Neighbours", "topics": [ "knnCMA,data.frame,missing,formula-method", "knnCMA,ExpressionSet,character,missing-method", "knnCMA,matrix,factor,missing-method", "knnCMA,matrix,numeric,missing-method", "knnCMA-methods" ] }, { "page": "LassoCMA", "title": "L1 penalized logistic regression", "topics": [ "LassoCMA" ] }, { "page": "LassoCMA-methods", "title": "L1 penalized logistic regression", "topics": [ "LassoCMA,data.frame,missing,formula-method", "LassoCMA,ExpressionSet,character,missing-method", "LassoCMA,matrix,factor,missing-method", "LassoCMA,matrix,numeric,missing-method", "LassoCMA-methods" ] }, { "page": "ldaCMA", "title": "Linear Discriminant Analysis", "topics": [ "ldaCMA" ] }, { "page": "ldaCMA-methods", "title": "Linear Discriminant Analysis", "topics": [ "ldaCMA,data.frame,missing,formula-method", 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"L2 penalized logistic regression", "topics": [ "plrCMA,data.frame,missing,formula-method", "plrCMA,ExpressionSet,character,missing-method", "plrCMA,matrix,factor,missing-method", "plrCMA,matrix,numeric,missing-method", "plrCMA-methods" ] }, { "page": "pls_ldaCMA", "title": "Partial Least Squares combined with Linear Discriminant Analysis", "topics": [ "pls_ldaCMA" ] }, { "page": "pls_ldaCMA-methods", "title": "Partial Least Squares combined with Linear Discriminant Analysis", "topics": [ "pls_ldaCMA,data.frame,missing,formula-method", "pls_ldaCMA,ExpressionSet,character,missing-method", "pls_ldaCMA,matrix,factor,missing-method", "pls_ldaCMA,matrix,numeric,missing-method", "pls_ldaCMA-methods" ] }, { "page": "pls_lrCMA", "title": "Partial Least Squares followed by logistic regression", "topics": [ "pls_lrCMA" ] }, { "page": "pls_lrCMA-methods", "title": "Partial Least Squares followed by logistic regression", "topics": [ "pls_lrCMA,data.frame,missing,formula-method", "pls_lrCMA,ExpressionSet,character,missing-method", "pls_lrCMA,matrix,factor,missing-method", "pls_lrCMA,matrix,numeric,missing-method", "pls_lrCMA-methods" ] }, { "page": "pls_rfCMA", "title": "Partial Least Squares followed by random forests", "topics": [ "pls_rfCMA" ] }, { "page": "pls_rfCMA-methods", "title": "Partial Least Squares followed by random forests", "topics": [ "pls_rfCMA,data.frame,missing,formula-method", "pls_rfCMA,ExpressionSet,character,missing-method", "pls_rfCMA,matrix,factor,missing-method", "pls_rfCMA,matrix,numeric,missing-method", "pls_rfCMA-methods" ] }, { "page": "pnnCMA", "title": "Probabilistic Neural Networks", "topics": [ "pnnCMA" ] }, { "page": "pnnCMA-methods", "title": "Probabilistic Neural Networks", "topics": [ "pnnCMA,data.frame,missing,formula-method", "pnnCMA,ExpressionSet,character,missing-method", "pnnCMA,matrix,factor,missing-method", "pnnCMA,matrix,numeric,missing-method", "pnnCMA-methods" ] }, { "page": "prediction", "title": "General method for predicting classes of new observations", "topics": [ "prediction" ] }, { "page": "prediction-methods", "title": "General method for predicting class lables of new observations", "topics": [ "prediction,data.frame,missing,data.frame,formula-method", "prediction,ExpressionSet,character,ExpressionSet,missing-method", "prediction,matrix,ANY,matrix,missing-method", "prediction-methods" ] }, { "page": "predoutput-class", "title": "\"predoutput\"", "topics": [ "predoutput", "predoutput-class", "show,predoutput-method" ] }, { "page": "qdaCMA", "title": "Quadratic Discriminant Analysis", "topics": [ "qdaCMA" ] }, { "page": "qdaCMA-methods", "title": "Quadratic Discriminant Analysis", "topics": [ "qdaCMA,data.frame,missing,formula-method", "qdaCMA,ExpressionSet,character,missing-method", "qdaCMA,matrix,factor,missing-method", "qdaCMA,matrix,numeric,missing-method", "qdaCMA-methods" ] }, { "page": "rfCMA", "title": "Classification based on Random Forests", "topics": [ "rfCMA" ] }, { "page": "rfCMA-methods", "title": "Classification based on Random Forests", "topics": [ "rfCMA,data.frame,missing,formula-method", "rfCMA,ExpressionSet,character,missing-method", "rfCMA,matrix,factor,missing-method", "rfCMA,matrix,numeric,missing-method", "rfCMA-methods" ] }, { "page": "roc", "title": "Receiver Operator Characteristic", "topics": [ "roc", "roc,cloutput-method" ] }, { "page": "scdaCMA", "title": "Shrunken Centroids Discriminant Analysis", "topics": [ "scdaCMA" ] }, { "page": "scdaCMA-methods", "title": "Shrunken Centroids Discriminant Analysis", "topics": [ "scdaCMA,data.frame,missing,formula-method", "scdaCMA,ExpressionSet,character,missing-method", "scdaCMA,matrix,factor,missing-method", "scdaCMA,matrix,numeric,missing-method", "scdaCMA-methods" ] }, { "page": "shrinkldaCMA", "title": "Shrinkage linear discriminant analysis", "topics": [ "shrinkldaCMA" ] }, { "page": "shrinkldaCMA-methods", "title": "Shrinkage linear discriminant analysis", "topics": [ "shrinkldaCMA,data.frame,missing,formula-method", "shrinkldaCMA,ExpressionSet,character,missing-method", "shrinkldaCMA,matrix,factor,missing-method", "shrinkldaCMA,matrix,numeric,missing-method", "shrinkldaCMA-methods" ] }, { "page": "summary", "title": "Summarize classifier evaluation", "topics": [ "summary,evaloutput-method" ] }, { "page": "svmCMA", "title": "Support Vector Machine", "topics": [ "svmCMA" ] }, { "page": "svmCMA-methods", "title": "Support Vector Machine", "topics": [ "svmCMA,data.frame,missing,formula-method", "svmCMA,ExpressionSet,character,missing-method", "svmCMA,matrix,factor,missing-method", "svmCMA,matrix,numeric,missing-method", "svmCMA-methods" ] }, { "page": "toplist", "title": "Display 'top' variables", "topics": [ "toplist", "toplist,genesel-method" ] }, { "page": "tune", "title": "Hyperparameter tuning for classifiers", "topics": [ "tune" ] }, { "page": "tune-methods", "title": "Hyperparameter tuning for classifiers", "topics": [ 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subsampling fold errors", "topics": [ "wmc" ] }, { "page": "wmc-methods", "title": "General method for tuning / selection bias correction based on a matrix of subsampling fold errors.", "topics": [ "wmc,matrix,numeric,numeric-method", "wmc-methods" ] }, { "page": "wmcr_result-class", "title": "\"wmcr.result\"", "topics": [ "show,wmcr.result-method", "wmcr.result", "wmcr.result-class" ] } ], "_rundeps": [ "Biobase", "BiocGenerics", "generics" ], "_vignettes": [ { "source": "CMA_vignette.rnw", "filename": "CMA_vignette.pdf", "title": "CMA Manual", "engine": "utils::Sweave", "headings": [ "Statistical background", "Class prediction based high-dimensional data with small samples", "Overview of CMA features", "Comparison with existing packages", "Example 1: Focussing on one method", "Example 2: classifier comparison" ], "created": "2013-11-01 20:04:08", "modified": "2013-11-01 20:04:08", "commits": 1 } ], "_score": 5.214843848047698, "_indexed": true, "_nocasepkg": "cma", "_universes": [ 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