| auroc | Area Under the Curve (AUC) and Receiver Operating... |
| background.predict | Calculate prediction areas |
| block.pls | N-integration with Projection to Latent Structures models... |
| block.plsda | N-integration with Projection to Latent Structures models... |
| block.spls | N-integration and feature selection with sparse Projection to... |
| block.splsda | N-integration and feature selection with Projection to Latent... |
| cim | Clustered Image Maps (CIMs) ("heat maps") |
| cimDiablo | Clustered Image Maps (CIMs) ("heat maps") for DIABLO |
| circosPlot | circosPlot for DIABLO |
| color.jet | Color Palette for mixOmics |
| color.mixo | #E69F00', # shiny orange #56B4E9' #Shiny blue |
| explained_variance | Calculation of explained variance |
| get.confusion_matrix | Create confusion table and calculate the Balanced Error Rate |
| imgCor | Image Maps of Correlation Matrices between two Data Sets |
| ipca | Independent Principal Component Analysis |
| logratio.transfo | Log-ratio transformation |
| map | Classification given Probabilities |
| mat.rank | Matrix Rank |
| mint.block.pls | NP-integration |
| mint.block.plsda | NP-integration with Discriminant Analysis |
| mint.block.spls | NP-integration for integration with variable selection |
| mint.block.splsda | NP-integration with Discriminant Analysis and variable... |
| mint.pca | P-integration with Principal Component Analysis |
| mint.pls | P-integration |
| mint.plsda | P-integration with Projection to Latent Structures models... |
| mint.spls | P-integration with variable selection |
| mint.splsda | P-integration with Discriminant Analysis and variable... |
| mixOmics | PLS-derived methods: one function to rule them all! |
| nearZeroVar | Identification of zero- or near-zero variance predictors |
| network | Relevance Network for (r)CCA and (s)PLS regression |
| nipals | Non-linear Iterative Partial Least Squares (NIPALS) algorithm |
| pca | Principal Components Analysis |
| perf | Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA,... |
| perf.mint.spls | Predict Method for (mint).(block).(s)pls(da) methods |
| plot | plot methods for mixOmics |
| plotArrow | Arrow sample plot |
| plotIndiv | Plot of Individuals (Experimental Units) |
| plotIndiv.mixo_pls | PLS sample plot methods |
| plotLoadings | Plot of Loading vectors |
| plot.rcc | Canonical Correlations Plot |
| plot.sgccda | Graphical output for the DIABLO framework |
| plotVar | Plot of Variables |
| pls | Partial Least Squares (PLS) Regression |
| plsda | Partial Least Squares Discriminant Analysis (PLS-DA). |
| print.mixo_pls | Print Methods for CCA, (s)PLS, PCA and Summary objects |
| rcc | Regularized Canonical Correlation Analysis |
| selectVar | 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) |
| study_split | divides a data matrix in a list of matrices defined by a... |
| summary.mixo_pls | Summary Methods for CCA and PLS objects |
| tune | Generic function to choose the parameters in the different... |
| tune.block.splsda | Tuning function for block.splsda method (N-integration with... |
| tune.mint.splsda | Estimate the parameters of mint.splsda method |
| tune.pca | Tune the number of principal components in PCA |
| tune.rcc | Estimate the parameters of regularization for Regularized CCA |
| tune.spls | Tuning functions for sPLS method |
| tune.splsda | Tuning functions for sPLS-DA method |
| tune.splslevel | Tuning functions for multilevel sPLS method |
| unmap | Dummy matrix for an outcome factor |
| vip | Variable Importance in the Projection (VIP) |
| withinVariation | Within matrix decomposition for repeated measurements... |
| wrapper.rgcca | mixOmics wrapper for Regularised Generalised Canonical... |
| wrapper.sgcca | mixOmics wrapper for Sparse Generalised Canonical Correlation... |
