Extensible Data Structures for Multivariate Analysis

Documentation for package ‘multivarious’ version 0.3.1

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-- 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.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 --

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

-- 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 --

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
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.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.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

-- 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