Package: MLInterfaces 1.85.0

Vincent Carey

MLInterfaces: Uniform interfaces to R machine learning procedures for data in Bioconductor containers

This package provides uniform interfaces to machine learning code for data in R and Bioconductor containers.

Authors:Vincent Carey [cre, aut], Jess Mar [aut], Jason Vertrees [ctb], Laurent Gatto [ctb], Phylis Atieno [ctb]

MLInterfaces_1.85.0.tar.gz
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MLInterfaces.pdf |MLInterfaces.html✨
MLInterfaces/json (API)
NEWS

# Install 'MLInterfaces' in R:

install.packages('MLInterfaces', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

brennan_2013_tabS7exc - Clinical characterization of 158 GBM samples from https://doi.org/10.1016/j.cell.2013.09.034 supp table S7

On BioConductor:MLInterfaces-1.85.0(bioc 3.20)MLInterfaces-1.84.0(bioc 3.19)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

bioconductor-package

143 exports 3.16 score 114 dependencies 6 dependents 10 mentions

Last updated 2 months agofrom:8cc43e4553

Exports:.accuracy .F1 .fn .fp .macroF1 .precision .recall .requireCachedGenerics .S3MethodsClasses .sensitivity .specificity .tn .tp acc adaI baggingI balKfold.xvspec BgbmI blackboostI confuMat confuTab cverrs DAB dlda2 dldaI es2df F1 fn fp fs.absT fs.probT fs.topVariance fsHistory gbm2 getConverter getDist getGrid getVarImp glmI.logistic hclustConverter hclustI hclustWidget kmeansConverter kmeansI knn.cv2 knn.cvI knn2 knnI ksvm2 ksvmI ldaI ldaI.predParms learnerIn3D lvq lvqI macroF1 makeClusteringSchema makeConfuMat makeLearnerSchema mapPSvec mkfmla MLearn mlearnWidget MLIConverter.Bgbm MLIConverter.blackboost MLIConverter.dlda MLIConverter.knn MLIConverter.knncv MLIConverter.ksvm MLIConverter.ldaPredMeth MLIConverter.logistic MLIConverter.naiveBayes MLIConverter.nnet MLIConverter.plsda MLIConverter.RAB MLIConverter.randomForest MLIConverter.rpart MLIConverter.selftesting MLIConverter.slda MLIConverter.svm MLIConverterListEl.class MLIConverterPredType.class MLIPredicter.knn MLIPredicter.ksvm MLIPredicter.naiveBayes MLIPredicter.nnet MLIPredicter.plsda MLIPredicter.randomForest MLIPredicter.svm naAs0 naiveBayesI nnetI pamConverter pamI partPlot planarPlot planarPlot2 plotOne plsda2 plsdaI plspinDF plspinHcube precision Predict predict.classifierOutput predict.dlda2 predict.gbm2 predict.knn.cv2 predict.knn2 predict.lvq predict.RAB predictions predScore predScores projectLearnerToGrid qdaI rab RAB RAB4es RABI randomForestI recall report RObject rpartI se2df sensitivity sldaI specificity standardMLIConverter svm2 svmI testPredictions testScores tn tonp tp trainInd trainPredictions trainScores x xvalLoop xvalSpec

Dependencies:abind annotate AnnotationDbi askpass assertthat base64enc Biobase BiocGenerics Biostrings bit bit64 blob bslib cachem class cli cluster commonmark cpp11 crayon crosstalk curl DBI DelayedArray DEoptimR digest diptest dplyr evaluate fansi fastmap flexmix fontawesome fpc fs gbm gdata genefilter generics GenomeInfoDb GenomeInfoDbData GenomicRanges ggvis glue gtools highr htmltools htmlwidgets httpuv httr hwriter igraph IRanges jquerylib jsonlite KEGGREST kernlab knitr later lattice lazyeval lifecycle magrittr MASS Matrix MatrixGenerics matrixStats mclust memoise mime mlbench modeltools nnet openssl pillar pkgconfig plogr pls png prabclus promises R6 rappdirs RColorBrewer Rcpp rlang rmarkdown robustbase rpart RSQLite S4Arrays S4Vectors sass sfsmisc shiny sourcetools SparseArray SummarizedExperiment survival sys threejs tibble tidyselect tinytex UCSC.utils utf8 vctrs withr xfun XML xtable XVector yaml zlibbioc

A machine learning tutorial tutorial: applications of the Bioconductor MLInterfaces package to gene expression data

VJ Carey, P. Atieno

Rendered fromMLprac2_2.Rmdusingknitr::rmarkdownon Jul 03 2024.

Last update: 2022-11-02
Started: 2022-11-02

MLInterfaces 2.0 -- a new design

VJ Carey, P. Atieno

Rendered fromMLint_devel.Rmdusingknitr::rmarkdownon Jul 03 2024.

Last update: 2022-11-02
Started: 2022-11-02

MLInterfaces Computer Cluster

Rendered fromxvalComputerClusters.Rnwusingutils::Sweaveon Jul 03 2024.

Last update: 2013-11-01
Started: 2013-11-01

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
generate a partition function for cross-validation, where the partitions are approximately balanced with respect to the distribution of a response variable	balKfold.xvspec
Clinical characterization of 158 GBM samples from https://doi.org/10.1016/j.cell.2013.09.034 supp table S7	brennan_2013_tabS7exc
Class "classifierOutput"	classifierOutput-class fsHistory,classifierOutput-method predictions predictions,classifierOutput-method predScore predScore,classifierOutput-method predScores predScores,classifierOutput-method RObject RObject,classifierOutput-method show,classifierOutput-method testPredictions testPredictions,classifierOutput-method testScores testScores,classifierOutput-method trainInd trainInd,classifierOutput-method trainPredictions trainPredictions,classifierOutput-method trainScores trainScores,classifierOutput-method
container for clustering outputs in uniform structure	clusteringOutput-class clusteringSchema-class getConverter getConverter,clusteringSchema-method getDist getDist,clusteringSchema-method plot,clusteringOutput,ANY-method prcomp-class prcompObj-class RObject,clusteringOutput-method show,clusteringOutput-method show,clusteringSchema-method silhouette-class
Compute the confusion matrix for a classifier.	confuMat confuMat,classifierOutput,character-method confuMat,classifierOutput,missing-method confuMat,classifierOutput,numeric-method confuMat,classifierOutput-method confuMat-methods
Compute confusion tables for a confusion matrix.	confuTab
support for feature selection in cross-validation	fs.absT fs.probT fs.topVariance
extract history of feature selection for a cross-validated machine learner	fsHistory
shiny-oriented GUI for cluster or classifier exploration	hclustWidget mlearnWidget
Class "learnerSchema" - convey information on a machine learning function to the MLearn wrapper	learnerSchema-class nonstandardLearnerSchema-class show,learnerSchema-method
revised MLearn interface for machine learning	adaI baggingI BgbmI blackboostI dlda dlda2 dldaI gbm2 glmI.logistic hclustI kmeansI knn.cv2 knn.cvI knn2 knnI ksvm2 ksvmI ldaI ldaI.predParms lvq lvqI makeLearnerSchema MLearn MLearn,formula,data.frame,clusteringSchema,ANY-method MLearn,formula,data.frame,learnerSchema,numeric-method MLearn,formula,data.frame,learnerSchema,xvalSpec-method MLearn,formula,ExpressionSet,character,numeric-method MLearn,formula,ExpressionSet,learnerSchema,numeric-method MLearn,formula,ExpressionSet,learnerSchema,xvalSpec-method MLearn,formula,SummarizedExperiment,learnerSchema,numeric-method MLearn_new naiveBayesI nnetI pamI plotXvalRDA plsda2 plsdaI qdaI rab RABI randomForestI rpartI sldaI standardMLIConverter svm2 svmI
MLInterfaces infrastructure	classifOutput clustOutput cverrs es2df getGrid getGrid,data.frame-method getGrid,ExpressionSet-method groupIndex hclustConverter kmeansConverter makeClusteringSchema makeConfuMat mapPSvec membMat MLIConverter.Bgbm MLIConverter.blackboost MLIConverter.dlda MLIConverter.knn MLIConverter.knncv MLIConverter.ksvm MLIConverter.ldaPredMeth MLIConverter.logistic MLIConverter.naiveBayes MLIConverter.nnet MLIConverter.plsda MLIConverter.RAB MLIConverter.randomForest MLIConverter.rpart MLIConverter.selftesting MLIConverter.slda MLIConverter.svm MLIConverterListEl.class MLIConverterPredType.class MLIPredicter.knn MLIPredicter.ksvm MLIPredicter.naiveBayes MLIPredicter.nnet MLIPredicter.plsda MLIPredicter.randomForest MLIPredicter.svm MLLabel MLOutput MLScore naAs0 pamConverter partPlot planarPlot2 plspinDF predict.dlda2 predict.gbm2 predict.knn.cv2 predict.knn2 predict.lvq predict.RAB probArray probMat qualScore se2df silhouetteVec SOMBout somout x
Assessing classifier performance	acc acc,table-method F1 F1,table-method fn fn,table-method fp fp,table-method macroF1 macroF1,classifierOutput,character-method macroF1,classifierOutput,missing-method macroF1,classifierOutput,numeric-method macroF1,numeric,numeric-method macroF1,table,missing-method macroF1-methods precision precision,classifierOutput,character-method precision,classifierOutput,missing-method precision,classifierOutput,numeric-method precision,table,missing-method precision-methods recall recall,classifierOutput,character-method recall,classifierOutput,missing-method recall,classifierOutput,numeric-method recall,table,missing-method recall-methods sensitivity sensitivity,classifierOutput,character-method sensitivity,classifierOutput,missing-method sensitivity,classifierOutput,numeric-method sensitivity,table,missing-method sensitivity-methods specificity specificity,table-method tn tn,table-method tp tp,table-method
Methods for Function planarPlot in Package `MLInterfaces'	planarPlot planarPlot,classifierOutput,data.frame,character-method planarPlot,classifierOutput,ExpressionSet,character-method planarPlot-methods
shiny app for interactive 3D visualization of mlbench hypercube	plspinHcube
Predict method for 'classifierOutput' objects	predict.classifierOutput
Class '"projectedLearner"'	learnerIn3D learnerIn3D,projectedLearner-method plot,projectedLearner,ANY-method plotOne plotOne,projectedLearner-method projectedLearner-class show,projectedLearner-method
create learned tesselation of feature space after PC transformation	projectLearnerToGrid
real adaboost (Friedman et al)	DAB mkfmla Predict Predict,daboostCont-method Predict,raboostCont-method RAB RAB4es tonp
Class "raboostCont" ~~~	daboostCont-class raboostCont-class show,raboostCont-method
Class "varImpStruct" - collect data on variable importance from various machine learning methods	getVarImp getVarImp,classifierOutput,logical-method getVarImp,classifierOutput,missing-method getVarImp,classifOutput,logical-method plot plot,varImpStruct,ANY-method plot,varImpStruct-method report report,varImpStruct-method show,varImpStruct-method varImpStruct-class
Cross-validation in clustered computing environments	xvalLoop xvalLoop,ANY-method
container for information specifying a cross-validated machine learning exercise	xvalSpec xvalSpec-class