Package: CMA 1.65.0
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.
Authors:
CMA_1.65.0.tar.gz
CMA_1.65.0.zip(r-4.5)CMA_1.65.0.zip(r-4.4)CMA_1.65.0.zip(r-4.3)
CMA_1.65.0.tgz(r-4.4-any)CMA_1.65.0.tgz(r-4.3-any)
CMA_1.65.0.tar.gz(r-4.5-noble)CMA_1.65.0.tar.gz(r-4.4-noble)
CMA_1.65.0.tgz(r-4.4-emscripten)CMA_1.65.0.tgz(r-4.3-emscripten)
CMA.pdf |CMA.html✨
CMA/json (API)
# Install 'CMA' in R: |
install.packages('CMA', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
On BioConductor:CMA-1.65.0(bioc 3.21)CMA-1.64.0(bioc 3.20)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 months agofrom:c6f5e4f1f1. Checks:OK: 1 NOTE: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 07 2024 |
R-4.5-win | NOTE | Dec 07 2024 |
R-4.5-linux | NOTE | Dec 07 2024 |
R-4.4-win | NOTE | Dec 07 2024 |
R-4.4-mac | NOTE | Dec 07 2024 |
R-4.3-win | NOTE | Dec 07 2024 |
R-4.3-mac | NOTE | Dec 07 2024 |
Exports:bestboxplotclassificationcomparecompBoostCMAdldaCMAElasticNetCMAevaluationfdaCMAflexdaCMAftableftestgbmCMAGenerateLearningsetsGeneSelectiongolubcritjoinknnCMAkruskaltestLassoCMAldaCMAlimmatestnnetCMAobsinfopknnCMAPlanarplotplotplrCMApls_ldaCMApls_lrCMApls_rfCMApnnCMApredictionqdaCMArfCMArferocscdaCMAshowshrinkldaCMAsummarysvmCMAtoplistttesttuneweighted.mcrwelchtestwilcoxtestwmc
Dependencies:BiobaseBiocGenericsgenerics
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Synthesis of microarray-based classification | CMA-package CMA |
Barplot of variable importance | plot,genesel,missing-method plot,genesel-method |
Show best hyperparameter settings | best best,tuningresult-method |
Make a boxplot of the classifier evaluation | boxplot,evaloutput-method |
General method for classification with various methods | classification |
General method for classification with various methods | classification,data.frame,missing,formula-method classification,ExpressionSet,character,missing-method classification,matrix,factor,missing-method classification,matrix,numeric,missing-method classification-methods |
"cloutput" | cloutput cloutput-class show,cloutput-method |
"clvarseloutput" | clvarseloutput clvarseloutput-class |
Compare different classifiers | compare |
Compare different classifiers | compare,list-method compare-methods |
Componentwise Boosting | compBoostCMA |
Componentwise Boosting | compBoostCMA,data.frame,missing,formula-method compBoostCMA,ExpressionSet,character,missing-method compBoostCMA,matrix,factor,missing-method compBoostCMA,matrix,numeric,missing-method compBoostCMA-methods |
Diagonal Discriminant Analysis | dldaCMA |
Diagonal Discriminant Analysis | dldaCMA,data.frame,missing,formula-method dldaCMA,ExpressionSet,character,missing-method dldaCMA,matrix,factor,missing-method dldaCMA,matrix,numeric,missing-method dldaCMA-methods |
Classfication and variable selection by the ElasticNet | ElasticNetCMA |
Classfication and variable selection by the ElasticNet | ElasticNetCMA,data.frame,missing,formula-method ElasticNetCMA,ExpressionSet,character,missing-method ElasticNetCMA,matrix,factor,missing-method ElasticNetCMA,matrix,numeric,missing-method ElasticNetCMA-methods |
"evaloutput" | evaloutput evaloutput-class obsinfo,evaloutput-method show,evaloutput-method |
Evaluation of classifiers | evaluation |
Evaluation of classifiers | evaluation,list-method evaluation-methods |
Fisher's Linear Discriminant Analysis | fdaCMA |
Fisher's Linear Discriminant Analysis | fdaCMA,data.frame,missing,formula-method fdaCMA,ExpressionSet,character,missing-method fdaCMA,matrix,factor,missing-method fdaCMA,matrix,numeric,missing-method fdaCMA-methods |
Filter functions for Gene Selection | ftest golubcrit kruskaltest limmatest rfe shrinkcat ttest welchtest wilcoxtest |
Flexible Discriminant Analysis | flexdaCMA |
Flexible Discriminant Analysis | flexdaCMA,data.frame,missing,formula-method flexdaCMA,ExpressionSet,character,missing-method flexdaCMA,matrix,factor,missing-method flexdaCMA,matrix,numeric,missing-method flexdaCMA-methods |
Cross-tabulation of predicted and true class labels | ftable,cloutput-method |
Tree-based Gradient Boosting | gbmCMA |
Tree-based Gradient Boosting | gbmCMA,data.frame,missing,formula-method gbmCMA,ExpressionSet,character,missing-method gbmCMA,matrix,factor,missing-method gbmCMA,matrix,numeric,missing-method gbmCMA-methods |
Repeated Divisions into learn- and tets sets | GenerateLearningsets |
"genesel" | genesel genesel-class show,genesel-method |
General method for variable selection with various methods | GeneSelection |
General method for variable selection with various methods | GeneSelection,data.frame,missing,formula-method GeneSelection,ExpressionSet,character,missing-method GeneSelection,matrix,factor,missing-method GeneSelection,matrix,numeric,missing-method GeneSelection-methods |
ALL/AML dataset of Golub et al. (1999) | golub |
Internal functions | bklr bklr.predict bkreg care.dev care.exp characterplot mklr mklr.predict mkreg my.care.exp plotprob ROCinternal roundvector rowswaps safeexp |
Combine list elements returned by the method classification | join |
Combine list elements returned by the method classification | join,list-method join-methods |
Small blue round cell tumor dataset of Khan et al. (2001) | khan |
Nearest Neighbours | knnCMA |
Nearest Neighbours | knnCMA,data.frame,missing,formula-method knnCMA,ExpressionSet,character,missing-method knnCMA,matrix,factor,missing-method knnCMA,matrix,numeric,missing-method knnCMA-methods |
L1 penalized logistic regression | LassoCMA |
L1 penalized logistic regression | LassoCMA,data.frame,missing,formula-method LassoCMA,ExpressionSet,character,missing-method LassoCMA,matrix,factor,missing-method LassoCMA,matrix,numeric,missing-method LassoCMA-methods |
Linear Discriminant Analysis | ldaCMA |
Linear Discriminant Analysis | ldaCMA,data.frame,missing,formula-method ldaCMA,ExpressionSet,character,missing-method ldaCMA,matrix,factor,missing-method ldaCMA,matrix,numeric,missing-method ldaCMA-methods |
"learningsets" | learningsets learningsets-class show,learningsets-method |
Feed-forward Neural Networks | nnetCMA |
Feed-Forward Neural Networks | nnetCMA,data.frame,missing,formula-method nnetCMA,ExpressionSet,character,missing-method nnetCMA,matrix,factor,missing-method nnetCMA,matrix,numeric,missing-method nnetCMA-methods |
Classifiability of observations | obsinfo |
Probabilistic Nearest Neighbours | pknnCMA |
Probabilistic nearest neighbours | pknnCMA,data.frame,missing,formula-method pknnCMA,ExpressionSet,character,missing-method pknnCMA,matrix,factor,missing-method pknnCMA,matrix,numeric,missing-method pknnCMA-methods |
Visualize Separability of different classes | Planarplot |
Visualize Separability of different classes | Planarplot,data.frame,missing,formula-method Planarplot,ExpressionSet,character,missing-method Planarplot,matrix,factor,missing-method Planarplot,matrix,numeric,missing-method Planarplot-methods |
Probability plot | plot,cloutput,missing-method plot,cloutput-method |
Visualize results of tuning | plot,tuningresult,missing-method plot,tuningresult-method |
L2 penalized logistic regression | plrCMA |
L2 penalized logistic regression | plrCMA,data.frame,missing,formula-method plrCMA,ExpressionSet,character,missing-method plrCMA,matrix,factor,missing-method plrCMA,matrix,numeric,missing-method plrCMA-methods |
Partial Least Squares combined with Linear Discriminant Analysis | pls_ldaCMA |
Partial Least Squares combined with Linear Discriminant Analysis | 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 |
Partial Least Squares followed by logistic regression | pls_lrCMA |
Partial Least Squares followed by logistic regression | 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 |
Partial Least Squares followed by random forests | pls_rfCMA |
Partial Least Squares followed by random forests | 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 |
Probabilistic Neural Networks | pnnCMA |
Probabilistic Neural Networks | pnnCMA,data.frame,missing,formula-method pnnCMA,ExpressionSet,character,missing-method pnnCMA,matrix,factor,missing-method pnnCMA,matrix,numeric,missing-method pnnCMA-methods |
General method for predicting classes of new observations | prediction |
General method for predicting class lables of new observations | prediction,data.frame,missing,data.frame,formula-method prediction,ExpressionSet,character,ExpressionSet,missing-method prediction,matrix,ANY,matrix,missing-method prediction-methods |
"predoutput" | predoutput predoutput-class show,predoutput-method |
Quadratic Discriminant Analysis | qdaCMA |
Quadratic Discriminant Analysis | qdaCMA,data.frame,missing,formula-method qdaCMA,ExpressionSet,character,missing-method qdaCMA,matrix,factor,missing-method qdaCMA,matrix,numeric,missing-method qdaCMA-methods |
Classification based on Random Forests | rfCMA |
Classification based on Random Forests | rfCMA,data.frame,missing,formula-method rfCMA,ExpressionSet,character,missing-method rfCMA,matrix,factor,missing-method rfCMA,matrix,numeric,missing-method rfCMA-methods |
Receiver Operator Characteristic | roc roc,cloutput-method |
Shrunken Centroids Discriminant Analysis | scdaCMA |
Shrunken Centroids Discriminant Analysis | scdaCMA,data.frame,missing,formula-method scdaCMA,ExpressionSet,character,missing-method scdaCMA,matrix,factor,missing-method scdaCMA,matrix,numeric,missing-method scdaCMA-methods |
Shrinkage linear discriminant analysis | shrinkldaCMA |
Shrinkage linear discriminant analysis | shrinkldaCMA,data.frame,missing,formula-method shrinkldaCMA,ExpressionSet,character,missing-method shrinkldaCMA,matrix,factor,missing-method shrinkldaCMA,matrix,numeric,missing-method shrinkldaCMA-methods |
Summarize classifier evaluation | summary,evaloutput-method |
Support Vector Machine | svmCMA |
Support Vector Machine | svmCMA,data.frame,missing,formula-method svmCMA,ExpressionSet,character,missing-method svmCMA,matrix,factor,missing-method svmCMA,matrix,numeric,missing-method svmCMA-methods |
Display 'top' variables | toplist toplist,genesel-method |
Hyperparameter tuning for classifiers | tune |
Hyperparameter tuning for classifiers | tune,data.frame,missing,formula-method tune,ExpressionSet,character,missing-method tune,matrix,factor,missing-method tune,matrix,numeric,missing-method tune-methods |
"tuningresult" | show,tuningresult-method tuningresult tuningresult-class |
"varseloutput" | varseloutput varseloutput-class |
Tuning / Selection bias correction | weighted.mcr |
General method for tuning / selection bias correction | weighted.mcr,character,character,missing,character,matrix,factor-method weighted.mcr,character,character,numeric,character,matrix,factor-method weighted.mcr,character,character,numeric,character,matrix,numeric-method weighted.mcr-methods |
Tuning / Selection bias correction based on matrix of subsampling fold errors | wmc |
General method for tuning / selection bias correction based on a matrix of subsampling fold errors. | wmc,matrix,numeric,numeric-method wmc-methods |
"wmcr.result" | show,wmcr.result-method wmcr.result wmcr.result-class |