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
biplot.pca              Biplot for PCA Objects (Enhanced with ggrepel)
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_pca           Fast, Exact Bootstrap for PCA Results from
                        'pca' function
bootstrap_plsc          Bootstrap inference for PLSC loadings
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_.
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
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
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
effect                  Extract a named effect from a fitted model
effect_operator         Construct an effect operator
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
group_means             Compute column-wise mean in X for each factor
                        level of Y
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_transform       Inverse transform data using a fitted
                        preprocessing pipeline
is_orthogonal           is it orthogonal
is_orthogonal.projector
                        Stricter check for true orthogonality
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
nblocks                 get the number of blocks
ncomp                   Get the number of components
nystrom_approx          Nyström approximation for kernel-based
                        decomposition (Unified Version)
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_test               Generic Permutation-Based Test
perm_test.effect_operator
                        Permutation test for an effect operator
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.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
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
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
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
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
variables_used          Identify Original Variables Used by a Projector
vars_for_component      Identify Original Variables for a Specific
                        Component
