R: Extensible Data Structures for Multivariate Analysis

# Extensible Data Structures for Multivariate Analysis

## Documentation for package ‘multivarious’ version 0.3.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A B C D E F G I M N P R S T V misc

## -- A --

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

## -- B --

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.effect_operator | Bootstrap stability summaries for an effect operator
bootstrap.plsc | Bootstrap Resampling for Multivariate Models
bootstrap_pca | Fast, Exact Bootstrap for PCA Results from 'pca' function
bootstrap_plsc | Bootstrap inference for PLSC loadings

## -- C --

cca | Canonical Correlation Analysis (CCA)
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 | Construct a 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++ (cPCA++) to find directions that capture variation enriched in a "foreground" dataset relative to a "background" dataset. This implementation follows the cPCA++ approach which directly solves the generalized eigenvalue problem Rf v = lambda Rb v, where Rf and Rb are the covariance matrices of the foreground and background data, centered using the _background mean_.
cross_projector | Two-way (cross) projection to latent components
cv | Cross-validation Framework
cv_generic | Generic cross-validation engine

## -- D --

discriminant_projector | Construct a Discriminant Projector

## -- E --

effect | Extract a named effect from a fitted model
effect_operator | Construct an effect operator

## -- F --

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

## -- G --

geneig | Generalized Eigenvalue Decomposition
group_means | Compute column-wise mean in X for each factor level of Y

## -- I --

identity_basis | Identity basis specification
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_projection.projector | Inverse of the Component Matrix
inverse_transform | Inverse transform data using a fitted preprocessing pipeline
is_orthogonal | is it orthogonal
is_orthogonal.projector | Stricter check for true orthogonality

## -- M --

measure_interblock_transfer_error | Compute inter-block transfer error metrics for a cross_projector
measure_reconstruction_error | Compute reconstruction-based error metrics
mixed_regress | Mixed-effect multivariate regression
multiblock_biprojector | Create a Multiblock Bi-Projector
multiblock_projector | Create a Multiblock Projector

## -- N --

nblocks | get the number of blocks
ncomp | Get the number of components
nystrom_approx | Nyström approximation for kernel-based decomposition (Unified Version)

## -- P --

partial_inverse_projection | Partial Inverse Projection of a Columnwise Subset of Component Matrix
partial_inverse_projection.cross_projector | Partial Inverse Projection of a Subset of the Loading Matrix in cross_projector
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_ci.pca | Permutation Confidence Intervals
perm_test | Generic Permutation-Based Test
perm_test.cross_projector | Generic Permutation-Based Test
perm_test.discriminant_projector | Generic Permutation-Based Test
perm_test.effect_operator | Permutation test for an effect operator
perm_test.multiblock_biprojector | Generic Permutation-Based Test
perm_test.pca | 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 or Euclid
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.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.cross_projector | project a cross_projector instance
project.nystrom_approx | Project new data using a Nyström approximation model
projector | Construct a 'projector' instance
project_block | Project a single "block" of data onto the subspace
project_block.multiblock_projector | Project Data onto a Specific Block
project_vars | Project one or more variables onto a subspace

## -- R --

rank_score | Calculate Rank Score for Predictions
reconstruct | Reconstruct the data
reconstruct.composed_projector | Reconstruct Data from Scores using a Composed Projector
reconstruct.pca | Reconstruct Data from PCA Results
reconstruct.regress | Reconstruct fitted or subsetted outputs for a 'regress' object
reconstruct_new | Reconstruct new data in a model's subspace
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 return residual matrix
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

## -- S --

scores | Retrieve the component scores
scores.cca | Extract scores from a CCA fit
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
shared_pca | Shared PCA basis specification
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
supplied_basis | Supplied basis specification
svd_wrapper | Singular Value Decomposition (SVD) Wrapper

## -- T --

topk | top-k accuracy indicator
transfer | Transfer data from one domain/block to another via a latent space
transfer.cross_projector | Transfer from X domain to Y domain (or vice versa) in a cross_projector
transform | Transform data using a fitted preprocessing pipeline
transpose | Transpose a model
truncate | truncate a component fit
truncate.composed_projector | Truncate a Composed Projector

## -- V --

variables_used | Identify Original Variables Used by a Projector
variables_used.composed_projector | Identify Original Variables Used by a Projector
vars_for_component | Identify Original Variables for a Specific Component
vars_for_component.composed_projector | Identify Original Variables for a Specific Component

## -- misc --

%>>% | Compose Multiple Partial Projectors