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 |
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_pca | Fast, Exact Bootstrap for PCA Results from 'pca' function |
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.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++... |
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 |
feature_importance | Evaluate feature importance |
feature_importance.classifier | Evaluate Feature Importance for a Classifier |
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 |
init_transform | initialize a transform |
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 |
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... |
measure_reconstruction_error | Compute reconstruction-based error metrics |
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_inverse_projection.cross_projector | Partial Inverse Projection of a Subset of the Loading Matrix... |
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 |
predict.classifier | Predict Class Labels using a Classifier Object |
predict.discriminant_projector | Predict method for a discriminant_projector, supporting LDA... |
predict.rf_classifier | Predict Class Labels using a Random Forest Classifier Object |
prep | prepare a dataset by applying a pre-processing pipeline |
prinang | Calculate Principal Angles Between Subspaces |
principal_angles | Principal angles (two sub‑spaces) |
print.bootstrap_pca_result | Print method for bootstrap_pca_result |
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_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 |
project.nystrom_approx | Project new data using a Nyström approximation model |
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 |
reconstruct.composed_projector | Reconstruct Data from Scores using a Composed Projector |
reconstruct_new | Reconstruct new data in a model's subspace |
reconstruct.pca | Reconstruct Data from PCA Results |
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 |
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 |
robust_inv_vTv | Possibly use ridge-regularized inversion of crossprod(v) |
rotate | Rotate a Component Solution |
rotate.pca | Rotate PCA Loadings |
scores | Retrieve the component scores |
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 |
topk | top-k accuracy indicator |
transfer | Transfer data from one domain/block to another via a latent... |
transfer.cross_projector | Transfer from X domain to Y domain (or vice versa) in a... |
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 |
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