Package index • multivarious

Model construction

Functions that compute multivariate decompositions

pca(): Principal Components Analysis (PCA)
plsc(): Partial Least Squares Correlation (PLSC)
cca(): Canonical Correlation Analysis (CCA)
svd_wrapper(): Singular Value Decomposition (SVD) Wrapper
regress(): Multi-output linear regression
mixed_regress(): Mixed-effect multivariate regression
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.
geneig(): Generalized Eigenvalue Decomposition
identity_basis(): Identity basis specification
shared_pca(): Shared PCA basis specification
supplied_basis(): Supplied basis specification

Model classes for multivariate decompositions and extension

Generic S3 classes use to represented multivariate model fits

projector(): Construct a projector instance
bi_projector(): Construct a bi_projector instance
effect_operator(): Construct an effect operator
bi_projector_union(): A Union of Concatenated bi_projector Fits
discriminant_projector(): Construct a Discriminant Projector
cross_projector(): Two-way (cross) projection to latent components
multiblock_biprojector(): Create a Multiblock Bi-Projector
multiblock_projector(): Create a Multiblock Projector

Model Fitting and Projections

Functions for fitting models and applying projections.

project(): New sample projection
effect(): Extract a named effect from a fitted model
residualize(): Compute a regression model for each column in a matrix and return residual matrix
partial_project(): Partially project a new sample onto subspace
partial_projector(): Construct a partial projector
project_block(): Project a single "block" of data onto the subspace
project_vars(): Project one or more variables onto a subspace
transpose(): Transpose a model
reconstruct(): Reconstruct the data
inverse_projection(): Inverse of the Component Matrix
partial_inverse_projection(): Partial Inverse Projection of a Columnwise Subset of Component Matrix
compose_projector(): Compose Two Projectors
compose_partial_projector() `%>>%`: Compose Multiple Partial Projectors
refit(): refit a model
nystrom_approx(): Nyström approximation for kernel-based decomposition (Unified Version)
fit(): Fit a preprocessing pipeline
fit_transform(): Fit and transform data in one step
transform(): Transform data using a fitted preprocessing pipeline
inverse_transform(): Inverse transform data using a fitted preprocessing pipeline

Cross Projection

Functions for creating and working with cross_projectors for two-way projection between two sets of variables or features.

project(<cross_projector>): project a cross_projector instance
coef(<cross_projector>): Extract coefficients from a cross_projector object
reprocess(<cross_projector>): reprocess a cross_projector instance
shape(<cross_projector>): shape of a cross_projector instance
transfer(): Transfer data from one domain/block to another via a latent space
inverse_projection(<cross_projector>): Default inverse_projection method for cross_projector
partial_inverse_projection(<cross_projector>): Partial Inverse Projection of a Subset of the Loading Matrix in cross_projector
partial_project(<cross_projector>): Partially project data for a cross_projector

Model Components and Properties

Functions for working with model components and properties.

components(): get the components
scores(): Retrieve the component scores
scores(<cca>): Extract scores from a CCA fit
scores(<plsc>): Extract scores from a PLSC fit
std_scores(): Compute standardized component scores
sdev(): standard deviations
ncomp(): Get the number of components
shape(): Shape of the Projector
is_orthogonal(): is it orthogonal
truncate(): truncate a component fit
block_lengths(): get block_lengths
block_indices(): get block_indices
nblocks(): get the number of blocks
prinang(): Calculate Principal Angles Between Subspaces
principal_angles(): Principal angles (two sub‑spaces)
variables_used(): Identify Original Variables Used by a Projector
vars_for_component(): Identify Original Variables for a Specific Component

Rotation and Transformation

Functions for rotating and transforming model components.

rotate(): Rotate a Component Solution
apply_rotation(): Apply rotation

Resampling and Confidence Intervals

Functions for bootstrapping and estimating confidence intervals.

bootstrap(): Bootstrap Resampling for Multivariate Models
bootstrap(<effect_operator>): Bootstrap stability summaries for an effect operator
bootstrap_pca(): Fast, Exact Bootstrap for PCA Results from pca function
bootstrap_plsc(): Bootstrap inference for PLSC loadings
perm_ci(): Permutation Confidence Intervals
perm_test perm_test.pca perm_test.cross_projector perm_test.discriminant_projector perm_test.multiblock_biprojector: Generic Permutation-Based Test
perm_test(<effect_operator>): Permutation test for an effect operator
perm_test(<plsc>): Permutation test for PLSC latent variables
cv(): Cross-validation Framework
cv_generic(): Generic cross-validation engine

Classifier Construction

Functions for constructing classifiers.

classifier(): Construct a Classifier
classifier(<discriminant_projector>): Create a k-NN classifier for a discriminant projector
classifier(<multiblock_biprojector>): Multiblock Bi-Projector Classifier
rf_classifier(): construct a random forest wrapper classifier
rf_classifier(<projector>): Create a random forest classifier
predict(<classifier>): Predict Class Labels using a Classifier Object
feature_importance(): Evaluate feature importance
feature_importance(<classifier>): Evaluate Feature Importance for a Classifier

Model Diagnostics and Residuals

Functions for evaluating model fit and residuals.

residuals(): Obtain residuals of a component model fit
measure_reconstruction_error(): Compute reconstruction-based error metrics
measure_interblock_transfer_error(): Compute inter-block transfer error metrics for a cross_projector
pca_outliers(): PCA Outlier Diagnostics
rank_score(): Calculate Rank Score for Predictions
subspace_similarity(): Compute subspace similarity

Pre-processing

Functions for pre-processing data and managing pipelines.

center(): center a data matrix
pass(): a no-op pre-processing step
standardize(): center and scale each vector of a matrix
colscale(): scale a data matrix
prep(): prepare a dataset by applying a pre-processing pipeline
fresh(): Get a fresh pre-processing node cleared of any cached data
reprocess(): apply pre-processing parameters to a new data matrix
apply_transform(): apply a pre-processing transform
reverse_transform(): reverse a pre-processing transform
init_transform(): initialize a transform
concat_pre_processors(): bind together blockwise pre-processors
add_node(): add a pre-processing stage
preprocess(): Convenience function for preprocessing workflow
check_fitted(): Check if preprocessor is fitted and error if not
is_fitted(): Check if a preprocessing object is fitted
mark_fitted(): Enhanced fitted state tracking
get_fitted_state(): Get fitted state from attributes

Visualization

Functions for plotting and visualization.

biplot(<pca>): Biplot for PCA Objects (Enhanced with ggrepel)
screeplot(): Screeplot for PCA
screeplot(<pca>): Screeplot for PCA

Utilities

Utility functions.

group_means(): Compute column-wise mean in X for each factor level of Y
robust_inv_vTv(): Possibly use ridge-regularized inversion of crossprod(v)
topk(): top-k accuracy indicator
reconstruct_new(): Reconstruct new data in a model's subspace

Other

Other functions

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(<pre_processor>): Print a pre_processor object
print(<prepper>): Print a prepper pipeline
print(<regress>): Pretty Print Method for regress Objects
print(<rf_classifier>): Pretty Print Method for rf_classifier Objects
summary(<composed_projector>): Summarize a Composed Projector
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
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
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
project(): New sample projection
project(<cross_projector>): project a cross_projector instance
project(<nystrom_approx>): Project new data using a Nyström approximation model
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
projector(): Construct a projector instance
truncate(): truncate a component fit
truncate(<composed_projector>): Truncate a Composed Projector
block_indices(): get block_indices
block_indices(<multiblock_projector>): Extract the Block Indices from a Multiblock Projector
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
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
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
rotate(): Rotate a Component Solution
rotate(<pca>): Rotate PCA Loadings
std_scores(): Compute standardized component scores
std_scores(<svd>): Calculate Standardized Scores for SVD results
is_orthogonal(): is it orthogonal
is_orthogonal(<projector>): Stricter check for true orthogonality
add_node(): add a pre-processing stage
add_node(<prepper>): Add a pre-processing node to a pipeline
shape(): Shape of the Projector
shape(<cross_projector>): shape of a cross_projector instance
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