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A B C D E I K L M N P R S T U V W Y
| auroc | Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classification |
| auroc.mint.plsda | Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classification |
| auroc.mint.splsda | Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classification |
| auroc.plsda | Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classification |
| auroc.sgccda | Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classification |
| auroc.splsda | Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classification |
| block.pls | Horizontal Partial Least Squares (PLS) integration |
| block.plsda | Horizontal Partial Least Squares - Discriminant Analysis (PLS-DA) integration |
| block.spls | Horizontal sparse Partial Least Squares (sPLS) integration |
| block.splsda | Horizontal sparse Partial Least Squares - Discriminant Analysis (sPLS-DA) integration |
| breast.TCGA | Breast Cancer multi omics data from TCGA |
| breast.tumors | Human Breast Tumors Data |
| cim | Clustered Image Maps (CIMs) ("heat maps") |
| cimDiablo | Clustered Image Maps (CIMs) ("heat maps") for DIABLO |
| circosPlot | circosPlot for DIABLO |
| color.GreenRed | Color Palette for mixOmics |
| color.jet | Color Palette for mixOmics |
| color.mixo | Color Palette for mixOmics |
| color.spectral | Color Palette for mixOmics |
| diverse.16S | 16S microbiome data: most diverse bodysites from HMP |
| estim.regul | Estimate the parameters of regularization for Regularized CCA |
| estim.regul.default | Estimate the parameters of regularization for Regularized CCA |
| explained_variance | Calculation of explained variance |
| image.estim.regul | Plot the cross-validation score. |
| image.tune.rcc | Plot the cross-validation score. |
| imgCor | Image Maps of Correlation Matrices between two Data Sets |
| ipca | Independent Principal Component Analysis |
| Koren.16S | 16S microbiome atherosclerosis study |
| linnerud | Linnerud Dataset |
| liver.toxicity | Liver Toxicity Data |
| logratio.transfo | Log-ratio transformation |
| map | Classification given Probabilities |
| mat.rank | Matrix Rank |
| mint.block.pls | Horizontal and Vertical integration |
| mint.block.plsda | Horizontal and Vertical Discriminant Analysis integration |
| mint.block.spls | Horizontal and Vertical sparse integration with variable selection |
| mint.block.splsda | Horizontal and Vertical Discriminant Analysis integration with variable selection |
| mint.pca | Vertical Principal Component integration |
| mint.pls | Vertical integration |
| mint.plsda | Vertical Discriminant Analysis integration |
| mint.spls | Vertical integration with variable selection |
| mint.splsda | Vertical Discriminant Analysis integration with variable selection |
| mixOmics | PLS-derived methods: one function to rule them all |
| multidrug | Multidrug Resistence Data |
| nearZeroVar | Identification of zero- or near-zero variance predictors |
| network | Relevance Network for (r)CCA and (s)PLS regression |
| network.default | Relevance Network for (r)CCA and (s)PLS regression |
| network.pls | Relevance Network for (r)CCA and (s)PLS regression |
| network.rcc | Relevance Network for (r)CCA and (s)PLS regression |
| network.spls | Relevance Network for (r)CCA and (s)PLS regression |
| nipals | Non-linear Iterative Partial Least Squares (NIPALS) algorithm |
| nutrimouse | Nutrimouse Dataset |
| pca | Principal Components Analysis |
| pcatune | Tune the number of principal components in PCA |
| perf | Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO |
| perf.mint.splsda | Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO |
| perf.pls | Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO |
| perf.plsda | Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO |
| perf.sgccda | Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO |
| perf.spls | Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO |
| perf.splsda | Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO |
| plot.perf | Plot for model performance |
| plot.perf.mint.plsda.mthd | Plot for model performance |
| plot.perf.mint.splsda.mthd | Plot for model performance |
| plot.perf.pls.mthd | Plot for model performance |
| plot.perf.plsda.mthd | Plot for model performance |
| plot.perf.sgccda.mthd | Plot for model performance |
| plot.perf.spls.mthd | Plot for model performance |
| plot.perf.splsda.mthd | Plot for model performance |
| plot.rcc | Canonical Correlations Plot |
| plot.tune | Plot for model performance |
| plot.tune.splsda | Plot for model performance |
| plotArrow | Arrow sample plot |
| plotContrib | Contribution plot of variables |
| plotDiablo | Graphical output for the DIABLO framework |
| plotIndiv | Plot of Individuals (Experimental Units) |
| plotIndiv.mint.spls | Plot of Individuals (Experimental Units) |
| plotIndiv.mint.splsda | Plot of Individuals (Experimental Units) |
| plotIndiv.pca | Plot of Individuals (Experimental Units) |
| plotIndiv.pls | Plot of Individuals (Experimental Units) |
| plotIndiv.rcc | Plot of Individuals (Experimental Units) |
| plotIndiv.rgcca | Plot of Individuals (Experimental Units) |
| plotIndiv.sgcca | Plot of Individuals (Experimental Units) |
| plotIndiv.sipca | Plot of Individuals (Experimental Units) |
| plotIndiv.spls | Plot of Individuals (Experimental Units) |
| plotLoadings | Plot of Loading vectors |
| plotLoadings.block.pls | Plot of Loading vectors |
| plotLoadings.block.plsda | Plot of Loading vectors |
| plotLoadings.block.spls | Plot of Loading vectors |
| plotLoadings.block.splsda | Plot of Loading vectors |
| plotLoadings.mint.pls | Plot of Loading vectors |
| plotLoadings.mint.plsda | Plot of Loading vectors |
| plotLoadings.mint.spls | Plot of Loading vectors |
| plotLoadings.mint.splsda | Plot of Loading vectors |
| plotLoadings.pca | Plot of Loading vectors |
| plotLoadings.pls | Plot of Loading vectors |
| plotLoadings.plsda | Plot of Loading vectors |
| plotLoadings.rcc | Plot of Loading vectors |
| plotLoadings.rgcca | Plot of Loading vectors |
| plotLoadings.sgcca | Plot of Loading vectors |
| plotLoadings.sgccda | Plot of Loading vectors |
| plotLoadings.spls | Plot of Loading vectors |
| plotLoadings.splsda | Plot of Loading vectors |
| plotVar | Plot of Variables |
| plotVar.pca | Plot of Variables |
| plotVar.pls | Plot of Variables |
| plotVar.plsda | Plot of Variables |
| plotVar.rcc | Plot of Variables |
| plotVar.rgcca | Plot of Variables |
| plotVar.sgcca | Plot of Variables |
| plotVar.spca | Plot of Variables |
| plotVar.spls | Plot of Variables |
| plotVar.splsda | Plot of Variables |
| pls | Partial Least Squares (PLS) Regression |
| plsda | Partial Least Squares Discriminant Analysis (PLS-DA). |
| predict.mint.block.pls | Predict Method for (mint).(block).(s)pls(da) methods |
| predict.mint.block.plsda | Predict Method for (mint).(block).(s)pls(da) methods |
| predict.mint.block.spls | Predict Method for (mint).(block).(s)pls(da) methods |
| predict.mint.block.splsda | Predict Method for (mint).(block).(s)pls(da) methods |
| predict.mint.pls | Predict Method for (mint).(block).(s)pls(da) methods |
| predict.mint.plsda | Predict Method for (mint).(block).(s)pls(da) methods |
| predict.mint.spls | Predict Method for (mint).(block).(s)pls(da) methods |
| predict.mint.splsda | Predict Method for (mint).(block).(s)pls(da) methods |
| predict.pls | Predict Method for (mint).(block).(s)pls(da) methods |
| predict.plsda | Predict Method for (mint).(block).(s)pls(da) methods |
| predict.spls | Predict Method for (mint).(block).(s)pls(da) methods |
| predict.splsda | Predict Method for (mint).(block).(s)pls(da) methods |
| Print Methods for CCA, (s)PLS, PCA and Summary objects | |
| print.pca | Print Methods for CCA, (s)PLS, PCA and Summary objects |
| print.pls | Print Methods for CCA, (s)PLS, PCA and Summary objects |
| print.rcc | Print Methods for CCA, (s)PLS, PCA and Summary objects |
| print.rgcca | Print Methods for CCA, (s)PLS, PCA and Summary objects |
| print.sgcca | Print Methods for CCA, (s)PLS, PCA and Summary objects |
| print.spca | Print Methods for CCA, (s)PLS, PCA and Summary objects |
| print.spls | Print Methods for CCA, (s)PLS, PCA and Summary objects |
| print.summary | Print Methods for CCA, (s)PLS, PCA and Summary objects |
| rcc | Regularized Canonical Correlation Analysis |
| rcc.default | Regularized Canonical Correlation Analysis |
| scatterutil.base | Graphical utility functions from ade4 |
| scatterutil.eti | Graphical utility functions from ade4 |
| scatterutil.grid | Graphical utility functions from ade4 |
| select.var | Output of selected variables |
| selectVar | Output of selected variables |
| selectVar.pca | Output of selected variables |
| selectVar.pls | Output of selected variables |
| selectVar.rgcca | Output of selected variables |
| selectVar.sgcca | Output of selected variables |
| selectVar.spls | Output of selected variables |
| sipca | Independent Principal Component Analysis |
| spca | Sparse Principal Components Analysis |
| spls | Sparse Partial Least Squares (sPLS) |
| splsda | Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) |
| srbct | Small version of the small round blue cell tumors of childhood data |
| stemcells | Human Stem Cells Data |
| study_split | divides a data matrix in a list of matrices defined by a factor |
| summary | Summary Methods for CCA and PLS objects |
| summary.pls | Summary Methods for CCA and PLS objects |
| summary.rcc | Summary Methods for CCA and PLS objects |
| summary.spls | Summary Methods for CCA and PLS objects |
| tune | Overall tuning function that can be used to tune several methods |
| tune.block.splsda | Tuning function for block.splsda method |
| tune.mint.splsda | Estimate the parameters of mint.splsda method |
| tune.multilevel | Tuning functions for multilevel analyses |
| tune.pca | Tune the number of principal components in PCA |
| tune.rcc | Estimate the parameters of regularization for Regularized CCA |
| tune.rcc.default | Estimate the parameters of regularization for Regularized CCA |
| tune.splsda | Tuning functions for sPLS-DA method |
| tune.splslevel | Tuning functions for multilevel analyses |
| unmap | Dummy matrix for an outcome factor |
| vac18 | Vaccine study Data |
| vac18.simulated | Simulated data based on the vac18 study for multilevel analysis |
| vip | Variable Importance in the Projection (VIP) |
| withinVariation | Within matrix decomposition for repeated measurements (cross-over design) |
| wrapper.rgcca | mixOmics wrapper for Regularised Generalised Canonical Correlation Analysis (rgcca) |
| wrapper.sgcca | mixOmics wrapper for Sparse Generalised Canonical Correlation Analysis (sgcca) |
| wrapper.sgccda | Horizontal sparse Partial Least Squares - Discriminant Analysis (sPLS-DA) integration |
| yeast | Yeast metabolomic study |