Man pages for bbuchsbaum/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 |
| 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 | PCA Bootstrap Resampling |
| center | center a data matrix |
| classifier | Construct a Classifier |
| classifier.discriminant_projector | Create a k-NN classifier for a discriminant projector |
| classifier.multiblock_biprojector | Multiblock Bi-Projector Classifier |
| classifier.projector | create classifier from a 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 |
| convert_domain | Transfer data from one input domain to another via common... |
| cPCA | Contrastive PCA (cPCA) with Adaptive Computation Methods |
| cross_projector | Two-way (cross) projection to latent components |
| discriminant_projector | Construct a Discriminant Projector |
| feature_importance | Evaluate feature importance |
| feature_importance.classifier | Evaluate Feature Importance |
| fresh | Get a fresh pre-processing node cleared of any cached data |
| fresh.prepper | Create a fresh pipeline from an existing prepper |
| geneig | Generalized Eigenvalue Decomposition |
| 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 |
| is_orthogonal | is it orthogonal |
| 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_project | Partially project a new sample onto subspace |
| partial_project.composed_partial_projector | Partial Project Through a Composed Partial Projector |
| partial_projector | Construct a partial projector |
| partial_projector.projector | construct a partial_projector from a 'projector' instance |
| pass | a no-op pre-processing step |
| pca | Principal Components Analysis (PCA) |
| perm_ci | Permutation Confidence Intervals |
| perm_ci.pca | Permutation-Based Confidence Intervals for PCA Components |
| predict.classifier | predict with a classifier object |
| prep | prepare a dataset by applying a pre-processing pipeline |
| prep.prepper | finalize a prepper pipeline |
| prinang | Compute principal angles for a set of subspaces |
| print.bi_projector | Pretty Print S3 Method for bi_projector Class |
| print.bi_projector_union | Pretty Print S3 Method for bi_projector_union Class |
| 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.prepper | Print a prepper pipeline |
| print.pre_processor | Print a pre_processor object |
| print.projector | Pretty Print Method for 'projector' Objects |
| print.regress | Pretty Print Method for 'regress' 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 |
| 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 |
| refit | refit a model |
| regress | Multi-output linear regression |
| relative_eigen | Relative Eigenanalysis with Ecosystem Integration |
| reprocess | apply pre-processing parameters to a new data matrix |
| reprocess.cross_projector | reprocess a cross_projector instance |
| 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 |
| rotate | Rotate a Component Solution |
| rotate.pca | Rotate PCA Loadings |
| scores | Retrieve the component scores |
| 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 |
| svd_wrapper | Singular Value Decomposition (SVD) Wrapper |
| transpose | Transpose a model |
| truncate | truncate a component fit |
