Package: FRESA.CAD 3.4.9

FRESA.CAD: Feature Selection Algorithms for Computer Aided Diagnosis

Contains a set of utilities for building and testing statistical models (linear, logistic,ordinal or COX) for Computer Aided Diagnosis/Prognosis applications. Utilities include data adjustment, univariate analysis, model building, model-validation, longitudinal analysis, reporting and visualization.

Authors:Jose Gerardo Tamez-Pena, Antonio Martinez-Torteya, Israel Alanis and Jorge Orozco

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NEWS

# Install 'FRESA.CAD' in R:
install.packages('FRESA.CAD', repos = c('https://josetamezpena.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/josetamezpena/fresa.cad/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

122 exports 5 stars 2.51 score 70 dependencies 31 scripts 451 downloads

Last updated 19 days agofrom:235d510776. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 30 2024
R-4.5-win-x86_64OKAug 30 2024
R-4.5-linux-x86_64OKAug 30 2024
R-4.4-win-x86_64OKAug 30 2024
R-4.4-mac-x86_64OKAug 30 2024
R-4.4-mac-aarch64OKAug 30 2024
R-4.3-win-x86_64OKAug 30 2024
R-4.3-mac-x86_64OKAug 30 2024
R-4.3-mac-aarch64OKAug 30 2024

Exports:adjustProbbackVarElimination_BinbackVarElimination_ResbaggedModelbaggedModelSbarPlotCiErrorBESSBESS_EBICBESS_GSECTIONBinaryBenchmarkbootstrapValidation_BinbootstrapValidation_ResbootstrapVarElimination_BinbootstrapVarElimination_ResBSWiMS.modelcalBinProbCalibrationProbPoissonRiskClassMetric95ciClustClassclusterISODATAconcordance95cicorrelated_RemoveCoxBenchmarkCoxRiskCalibrationcrossValidationFeatureSelection_BincrossValidationFeatureSelection_ResCVsignatureEmpiricalSurvDiffensemblePredictexpectedEventsPerIntervalfeatureAdjustmentfilteredFitForwardSelection.Model.BinForwardSelection.Model.ResFRESA.ModelFRESAScalegetKNNpredictionFromFormulagetLatentCoefficientsgetMedianLogisticCalibratedPredictiongetMedianSurvCalibratedPredictiongetObservedCoefgetSignaturegetVar.BingetVar.ResGLMNETGLMNET_ELASTICNET_1SEGLMNET_ELASTICNET_MINGLMNET_RIDGE_1SEGLMNET_RIDGE_MINGMVEBSWiMSGMVEClusterheatMapsHLCMHLCM_EMIDeAILAAimprovedResidualsjaccardMatrixKNN_methodLASSO_1SELASSO_MINlistTopCorrelatedVariablesLM_RIDGE_MINMAE95cimeanTimeToEventmetric95cimodelFittingmRMR.classic_FRESAmultivariate_BinEnsembleNAIVE_BAYESnearestCentroidnearestNeighborImputeOrdinalBenchmarkplotModels.ROCppoisGzeropredict.BAGGSpredict.CLUSTER_CLASSpredict.fitFRESApredict.FRESA_BESSpredict.FRESA_FILTERFITpredict.FRESA_GLMNETpredict.FRESA_HLCMpredict.FRESA_NAIVEBAYESpredict.FRESA_RIDGEpredict.FRESA_SVMpredict.FRESAKNNpredict.FRESAsignaturepredict.GMVEpredict.GMVE_BSWiMSpredict.LogitCalPredpredictDecorrelatepredictionStats_binarypredictionStats_ordinalpredictionStats_regressionpredictionStats_survivalrandomCVrankInverseNormalDataFrameRegresionBenchmarkreportEquivalentVariablesresidualForFRESARRPlotsignatureDistancesperman95cisummary.fitFRESAsummaryReporttimeSerieAnalysistrajectoriesPolyFeaturesTUNED_SVMuniRankVarunivariate_BinEnsembleunivariate_correlationunivariate_coxunivariate_DTSunivariate_KSunivariate_Logitunivariate_residualunivariate_Strataunivariate_tstudentunivariate_WilcoxonunivariateRankVariablesupdateModel.BinupdateModel.Res

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercolorspacedata.tabledigestevaluatefansifarverfastmapfontawesomeforeignFormulafsggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemiscToolsmunsellnlmennetpillarpkgconfigplyrpROCR6rappdirsRColorBrewerRcppRcppArmadillorlangrmarkdownrpartrstudioapisassscalesstringistringrtibbletinytexutf8vctrsviridisviridisLitewithrxfunyaml

Readme and manuals

Help Manual

Help pageTopics
FeatuRE Selection Algorithms for Computer-Aided Diagnosis (FRESA.CAD)FRESA.CAD-package FRESA.CAD
IDI/NRI-based backwards variable eliminationbackVarElimination_Bin
NeRI-based backwards variable eliminationbackVarElimination_Res
Get the bagged model from a list of modelsbaggedModel baggedModelS
Bar plot with error barsbarPlotCiError
Compare performance of different model fitting/filtering algorithmsBinaryBenchmark CoxBenchmark OrdinalBenchmark RegresionBenchmark
CV BeSS fitBESS BESS_EBIC BESS_GSECTION
Bootstrap validation of binary classification modelsbootstrapValidation_Bin
Bootstrap validation of regression modelsbootstrapValidation_Res
IDI/NRI-based backwards variable elimination with bootstrappingbootstrapVarElimination_Bin
NeRI-based backwards variable elimination with bootstrappingbootstrapVarElimination_Res
BSWiMS model selectionBSWiMS.model
Calibrates Predicted Binary ProbabilitiescalBinProb
Baseline hazard and interval time EstimationsCalibrationProbPoissonRisk CoxRiskCalibration
Data frame used in several examples of this packagecancerVarNames
Hybrid Hierarchical ModelingClustClass
Cluster Clustering using the Isodata ApproachclusterISODATA
IDI/NRI-based selection of a linear, logistic, or Cox proportional hazards regression model from a set of candidate variablescrossValidationFeatureSelection_Bin
NeRI-based selection of a linear, logistic, or Cox proportional hazards regression model from a set of candidate variablescrossValidationFeatureSelection_Res
Cross-validated SignatureCVsignature
Estimate the LR value and its associated p-valuesEmpiricalSurvDiff
The median prediction from a list of modelsensemblePredict
Adjust each listed variable to the provided set of covariatesfeatureAdjustment
A generic pipeline of Feature Selection, Transformation, Scale and fitfilteredFit
Univariate Filterscorrelated_Remove FilterUnivariate univariate_BinEnsemble univariate_correlation univariate_cox univariate_DTS univariate_KS univariate_Logit univariate_residual univariate_Strata univariate_tstudent univariate_Wilcoxon
IDI/NRI-based feature selection procedure for linear, logistic, and Cox proportional hazards regression modelsForwardSelection.Model.Bin
NeRI-based feature selection procedure for linear, logistic, or Cox proportional hazards regression modelsForwardSelection.Model.Res
Automated model selectionFRESA.Model
Data frame normalizationFRESAScale
Predict classification using KNNgetKNNpredictionFromFormula
Derived Features of the UPLTM transformgetLatentCoefficients getObservedCoef
Binary Predictions Calibration of Random CVgetMedianLogisticCalibratedPrediction getMedianSurvCalibratedPrediction
Returns a CV signature templategetSignature
Analysis of the effect of each term of a binary classification model by analysing its reclassification performancegetVar.Bin
Analysis of the effect of each term of a linear regression model by analysing its residualsgetVar.Res
GLMNET fit with feature selection"GLMNET GLMNET_ELASTICNET_1SE GLMNET_ELASTICNET_MIN GLMNET_RIDGE_1SE GLMNET_RIDGE_MIN LASSO_1SE LASSO_MIN
Hybrid Hierarchical Modeling with GMVE and BSWiMSGMVEBSWiMS
Set Clustering using the Generalized Minimum Volume Ellipsoid (GMVE)GMVECluster
Plot a heat map of selected variablesheatMaps
Latent class based modeling of binary outcomesHLCM HLCM_EM
Decorrelation of data framesIDeA ILAA predictDecorrelate
Estimate the significance of the reduction of predicted residualsimprovedResiduals
Jaccard Index of two labeled setsjaccardMatrix
KNN Setup for KNN predictionKNN_method
List the variables that are highly correlated with each otherlistTopCorrelatedVariables
Ridge Linear ModelsLM_RIDGE_MIN
Estimators and 95CIClassMetric95ci concordance95ci MAE95ci metric95ci sperman95ci
Fit a model to the datamodelFitting
FRESA.CAD wrapper of mRMRe::mRMR.classicmRMR.classic_FRESA
Multivariate Filtersmultivariate_BinEnsemble
Naive Bayes ModelingNAIVE_BAYES
Class Label Based on the Minimum Mahalanobis DistancenearestCentroid
nearest neighbor NA imputationnearestNeighborImpute
Plot ROC curves of bootstrap resultsplot plot.bootstrapValidation_Bin
Plot ROC curves of bootstrap resultsplot.bootstrapValidation_Res
Plot the results of the model selection benchmarkplot.FRESA_benchmark
Plot test ROC curves of each cross-validation modelplotModels.ROC
Probability of more than zero eventsadjustProb expectedEventsPerInterval meanTimeToEvent ppoisGzero
Predicts 'baggedModel' bagged modelspredict.BAGGS
Predicts 'ClustClass' outcomepredict.CLUSTER_CLASS
Linear or probabilistic predictionpredict predict.fitFRESA
Predicts 'BESS' modelspredict.FRESA_BESS
Predicts 'filteredFit' modelspredict.FRESA_FILTERFIT
Predicts GLMNET fitted objectspredict.FRESA_GLMNET
Predicts BOOST_BSWiMS modelspredict.FRESA_HLCM
Predicts 'NAIVE_BAYES' modelspredict.FRESA_NAIVEBAYES
Predicts 'LM_RIDGE_MIN' modelspredict.FRESA_RIDGE
Predicts 'TUNED_SVM' modelspredict.FRESA_SVM
Predicts 'class::knn' modelspredict.FRESAKNN
Predicts 'CVsignature' modelspredict.FRESAsignature
Predicts 'GMVECluster' clusterspredict.GMVE
Predicts 'GMVEBSWiMS' outcomepredict.GMVE_BSWiMS
Predicts calibrated probabilitiespredict.LogitCalPred
Prediction EvaluationpredictionStats_binary predictionStats_ordinal predictionStats_regression predictionStats_survival
Cross Validation of Prediction ModelsrandomCV
rank-based inverse normal transformation of the datarankInverseNormalDataFrame
Report the set of variables that will perform an equivalent IDI discriminant functionreportEquivalentVariables
Return residuals from predictionresidualForFRESA
Plot and Analysis of Indices of RiskRRPlot
Distance to the signature templatesignatureDistance
Generate a report of the results obtained using the bootstrapValidation_Bin functionsummary.bootstrapValidation_Bin
Returns the summary of the fitsummary summary.fitFRESA
Report the univariate analysis, the cross-validation analysis and the correlation analysissummaryReport
Fit the listed time series variables to a given modeltimeSerieAnalysis
Extract the per patient polynomial Coefficients of a feature trayectorytrajectoriesPolyFeatures
Tuned SVMTUNED_SVM
Univariate analysis of features (additional values returned)uniRankVar
Univariate analysis of featuresunivariateRankVariables
Update the univariate analysis using new dataupdate update.uniRankVar
Update the IDI/NRI-based model using new data or new threshold valuesupdateModel.Bin
Update the NeRI-based model using new data or new threshold valuesupdateModel.Res