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B C D E I L M N P R S T U V W Y
| bin.color | Internal Functions |
| breast.tumors | Human Breast Tumors Data |
| cim | Clustered Image Maps (CIMs) ("heat maps") |
| color.GreenRed | Color Palette for mixOmics |
| color.jet | Color Palette for mixOmics |
| color.mixo | Color Palette for mixOmics |
| color.spectral | Color Palette for mixOmics |
| cov2 | Internal Functions |
| defl.select | Internal Functions |
| deflation | Internal Functions |
| estim.regul | Estimate the parameters of regularization for Regularized CCA |
| estim.regul.default | Estimate the parameters of regularization for Regularized CCA |
| ica.def | Internal Functions |
| ica.par | Internal Functions |
| 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 |
| initsvd | Internal Functions |
| ipca | Independent Principal Component Analysis |
| l2.norm | Internal Functions |
| linnerud | Linnerud Dataset |
| liver.toxicity | Liver Toxicity Data |
| loo | Internal Functions |
| map | Internal Functions |
| mat.rank | Matrix Rank |
| Mfold | Internal Functions |
| miscrossprod | Internal Functions |
| multidrug | Multidrug Resistence Data |
| multilevel | Multilevel analysis for repeated measurements (cross-over design) |
| multilevel.spls | Multilevel analysis for repeated measurements (cross-over design) |
| multilevel.splsda | Multilevel analysis for repeated measurements (cross-over design) |
| 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 |
| pcasvd | Internal Functions |
| pcatune | Tune the number of principal components in PCA |
| perf | Compute evaluation criteria for PLS, sPLS, PLS-DA and sPLS-DA |
| perf.pls | Compute evaluation criteria for PLS, sPLS, PLS-DA and sPLS-DA |
| perf.plsda | Compute evaluation criteria for PLS, sPLS, PLS-DA and sPLS-DA |
| perf.spls | Compute evaluation criteria for PLS, sPLS, PLS-DA and sPLS-DA |
| perf.splsda | Compute evaluation criteria for PLS, sPLS, PLS-DA and sPLS-DA |
| plot.perf | Plot for model performance |
| plot.rcc | Canonical Correlations Plot |
| plotArrow | Arrow sample plot |
| plotContrib | Contribution plot of variables |
| plotIndiv | Plot of Individuals (Experimental Units) |
| 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.pls | Predict Method for PLS, sPLS, PLS-DA or sPLS-DA |
| predict.plsda | Predict Method for PLS, sPLS, PLS-DA or sPLS-DA |
| predict.spls | Predict Method for PLS, sPLS, PLS-DA or sPLS-DA |
| predict.splsda | Predict Method for PLS, sPLS, PLS-DA or sPLS-DA |
| 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 |
| prostate | Human Prostate Tumors Data |
| rcc | Regularized Canonical Correlation Analysis |
| rcc.default | Regularized Canonical Correlation Analysis |
| s.match | Plot of Paired Coordinates |
| scale2 | Internal Functions |
| 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.plsda | Output of selected variables |
| selectVar.rgcca | Output of selected variables |
| selectVar.sgcca | Output of selected variables |
| selectVar.sipca | Output of selected variables |
| sipca | Independent Principal Component Analysis |
| soft.threshold | Internal Functions |
| soft_thresholding_L1 | Internal Functions |
| sparsity | Internal Functions |
| 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 |
| 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 |
| tau.estimate | Internal Functions |
| 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.splsdalevel1 | Tuning functions for multilevel analyses |
| tune.splsdalevel2 | Tuning functions for multilevel analyses |
| 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 | (Generalised Canonical Correlation Analysis |
| wrapper.sgcca | (Generalised Canonical Correlation Analysis |
| wrapper.sgccda | (Generalised Canonical Correlation Analysis |
| yeast | Yeast metabolomic study |