Man pages for multivarious
Extensible Data Structures for Multivariate Analysis
| add_node | add a pre-processing stage |
| add_node.prepper | Add a pre-processing node to a pipeline |
| apply_rotation | Apply rotation |
| apply_transform | apply a pre-processing transform |
| biplot.pca | Biplot for PCA Objects (Enhanced with ggrepel) |
| bi_projector | Construct a bi_projector instance |
| bi_projector_union | A Union of Concatenated 'bi_projector' Fits |
| block_indices | get block_indices |
| block_indices.multiblock_projector | Extract the Block Indices from a Multiblock Projector |
| block_lengths | get block_lengths |
| bootstrap | Bootstrap Resampling for Multivariate Models |
| bootstrap_pca | Fast, Exact Bootstrap for PCA Results from 'pca' function |
| bootstrap_plsc | Bootstrap inference for PLSC loadings |
| center | center a data matrix |
| check_fitted | Check if preprocessor is fitted and error if not |
| classifier | Construct a Classifier |
| classifier.discriminant_projector | Create a k-NN classifier for a discriminant projector |
| classifier.multiblock_biprojector | Multiblock Bi-Projector Classifier |
| coef.composed_projector | Get Coefficients of a Composed Projector |
| coef.cross_projector | Extract coefficients from a cross_projector object |
| coef.multiblock_projector | Coefficients for a Multiblock Projector |
| colscale | scale a data matrix |
| components | get the components |
| compose_partial_projector | Compose Multiple Partial Projectors |
| compose_projector | Compose Two Projectors |
| concat_pre_processors | bind together blockwise pre-processors |
| cPCAplus | Contrastive PCA++ (cPCA++) Performs Contrastive PCA++... |
| cross_projector | Two-way (cross) projection to latent components |
| cv | Cross-validation Framework |
| cv_generic | Generic cross-validation engine |
| discriminant_projector | Construct a Discriminant Projector |
| feature_importance | Evaluate feature importance |
| feature_importance.classifier | Evaluate Feature Importance for a Classifier |
| fit | Fit a preprocessing pipeline |
| fit_transform | Fit and transform data in one step |
| fresh | Get a fresh pre-processing node cleared of any cached data |
| geneig | Generalized Eigenvalue Decomposition |
| get_fitted_state | Get fitted state from attributes |
| group_means | Compute column-wise mean in X for each factor level of Y |
| init_transform | initialize a transform |
| inverse_projection | Inverse of the Component Matrix |
| inverse_projection.composed_projector | Compute the Inverse Projection for a Composed Projector |
| inverse_projection.cross_projector | Default inverse_projection method for cross_projector |
| inverse_transform | Inverse transform data using a fitted preprocessing pipeline |
| is_fitted | Check if a preprocessing object is fitted |
| is_orthogonal | is it orthogonal |
| is_orthogonal.projector | Stricter check for true orthogonality |
| mark_fitted | Enhanced fitted state tracking |
| measure_interblock_transfer_error | Compute inter-block transfer error metrics for a... |
| measure_reconstruction_error | Compute reconstruction-based error metrics |
| multiblock_biprojector | Create a Multiblock Bi-Projector |
| multiblock_projector | Create a Multiblock Projector |
| nblocks | get the number of blocks |
| ncomp | Get the number of components |
| nystrom_approx | Nyström approximation for kernel-based decomposition (Unified... |
| partial_inverse_projection | Partial Inverse Projection of a Columnwise Subset of... |
| partial_inverse_projection.cross_projector | Partial Inverse Projection of a Subset of the Loading Matrix... |
| partial_inverse_projection.regress | Partial Inverse Projection for a 'regress' Object |
| partial_project | Partially project a new sample onto subspace |
| partial_project.composed_partial_projector | Partial Project Through a Composed Partial Projector |
| partial_project.cross_projector | Partially project data for a cross_projector |
| partial_projector | Construct a partial projector |
| pass | a no-op pre-processing step |
| pca | Principal Components Analysis (PCA) |
| pca_outliers | PCA Outlier Diagnostics |
| perm_ci | Permutation Confidence Intervals |
| perm_test | Generic Permutation-Based Test |
| perm_test.plsc | Permutation test for PLSC latent variables |
| plsc | Partial Least Squares Correlation (PLSC) |
| predict.classifier | Predict Class Labels using a Classifier Object |
| predict.discriminant_projector | Predict method for a discriminant_projector, supporting LDA... |
| predict.rf_classifier | Predict Class Labels using a Random Forest Classifier Object |
| prep | prepare a dataset by applying a pre-processing pipeline |
| preprocess | Convenience function for preprocessing workflow |
| prinang | Calculate Principal Angles Between Subspaces |
| principal_angles | Principal angles (two sub‑spaces) |
| print.bi_projector | Pretty Print S3 Method for bi_projector Class |
| print.bootstrap_pca_result | Print method for bootstrap_pca_result |
| print.classifier | Pretty Print Method for 'classifier' Objects |
| print.concat_pre_processor | Print a concat_pre_processor object |
| print.multiblock_biprojector | Pretty Print Method for 'multiblock_biprojector' Objects |
| print.pca | Print Method for PCA Objects |
| print.perm_test | Print Method for perm_test Objects |
| print.perm_test_pca | Print Method for perm_test_pca Objects |
| print.prepper | Print a prepper pipeline |
| print.pre_processor | Print a pre_processor object |
| print.regress | Pretty Print Method for 'regress' Objects |
| print.rf_classifier | Pretty Print Method for 'rf_classifier' Objects |
| project | New sample projection |
| project_block | Project a single "block" of data onto the subspace |
| project_block.multiblock_projector | Project Data onto a Specific Block |
| project.cross_projector | project a cross_projector instance |
| project.nystrom_approx | Project new data using a Nyström approximation model |
| projector | Construct a 'projector' instance |
| project_vars | Project one or more variables onto a subspace |
| rank_score | Calculate Rank Score for Predictions |
| reconstruct | Reconstruct the data |
| reconstruct.composed_projector | Reconstruct Data from Scores using a Composed Projector |
| reconstruct_new | Reconstruct new data in a model's subspace |
| reconstruct.pca | Reconstruct Data from PCA Results |
| reconstruct.regress | Reconstruct fitted or subsetted outputs for a 'regress'... |
| refit | refit a model |
| regress | Multi-output linear regression |
| reprocess | apply pre-processing parameters to a new data matrix |
| reprocess.cross_projector | reprocess a cross_projector instance |
| reprocess.nystrom_approx | Reprocess data for Nyström approximation |
| residualize | Compute a regression model for each column in a matrix and... |
| residuals | Obtain residuals of a component model fit |
| reverse_transform | reverse a pre-processing transform |
| rf_classifier | construct a random forest wrapper classifier |
| rf_classifier.projector | Create a random forest classifier |
| robust_inv_vTv | Possibly use ridge-regularized inversion of crossprod(v) |
| rotate | Rotate a Component Solution |
| rotate.pca | Rotate PCA Loadings |
| scores | Retrieve the component scores |
| scores.plsc | Extract scores from a PLSC fit |
| screeplot | Screeplot for PCA |
| screeplot.pca | Screeplot for PCA |
| sdev | standard deviations |
| shape | Shape of the Projector |
| shape.cross_projector | shape of a cross_projector instance |
| standardize | center and scale each vector of a matrix |
| std_scores | Compute standardized component scores |
| std_scores.svd | Calculate Standardized Scores for SVD results |
| subspace_similarity | Compute subspace similarity |
| summary.composed_projector | Summarize a Composed Projector |
| svd_wrapper | Singular Value Decomposition (SVD) Wrapper |
| topk | top-k accuracy indicator |
| transfer | Transfer data from one domain/block to another via a latent... |
| transfer.cross_projector | Transfer from X domain to Y domain (or vice versa) in a... |
| transform | Transform data using a fitted preprocessing pipeline |
| transpose | Transpose a model |
| truncate | truncate a component fit |
| truncate.composed_projector | Truncate a Composed Projector |
| variables_used | Identify Original Variables Used by a Projector |
| vars_for_component | Identify Original Variables for a Specific Component |
