add_node |
add a pre-processing stage |
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 |
block_indices |
get block_indices |
block_lengths |
get block_lengths |
bootstrap |
Bootstrap Resampling for Multivariate Models |
bootstrap.pca |
PCA Bootstrap Resampling |
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.cross_projector |
Extract coefficients from a cross_projector object |
colscale |
scale a data matrix |
components |
get the components |
compose_projector |
Compose Two Projectors |
compose_projectors |
Projector Composition |
concat_pre_processors |
bind together blockwise pre-processors |
convert_domain |
Transfer data from one input domain to another via common latent space |
cross_projector |
Two-way (cross) projection to latent components |
discriminant_projector |
Construct a Discriminant Projector |
fresh |
Get a fresh pre-processing node cleared of any cached data |
group_means |
Compute column-wise mean in X for each factor level of Y |
inverse_projection |
Inverse of the Component Matrix |
is_orthogonal |
is it orthogonal |
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_embedding |
Nystrom method for out-of-sample embedding |
partial_inverse_projection |
Partial Inverse Projection of a Columnwise Subset of Component Matrix |
partial_project |
Partially project a new sample onto subspace |
partial_projector |
Construct a partial projector |
partial_projector.projector |
construct a partial_projector from a 'projector' instance |
pass |
a no-op pre-processing step |
pca |
Principal Components Analysis (PCA) |
perm_ci |
Permutation Confidence Intervals |
predict.classifier |
predict with a classifier object |
prep |
prepare a dataset by applying a pre-processing pipeline |
prinang |
Compute principal angles for a set of subspaces |
print.bi_projector |
Pretty Print S3 Method for bi_projector Class |
print.bi_projector_union |
Pretty Print S3 Method for bi_projector_union Class |
print.classifier |
Pretty Print Method for 'classifier' Objects |
print.composed_projector |
Pretty Print Method for 'composed_projector' Objects |
print.multiblock_biprojector |
Pretty Print Method for 'multiblock_biprojector' Objects |
print.projector |
Pretty Print Method for 'projector' Objects |
project |
New sample projection |
project.cross_projector |
project a cross_projector instance |
projector |
Construct a 'projector' instance |
project_block |
Project a single "block" of data onto the subspace |
project_vars |
Project one or more variables onto a subspace |
reconstruct |
Reconstruct the data |
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 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 |
scores |
Retrieve the component scores |
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 |
svd_wrapper |
Singular Value Decomposition (SVD) Wrapper |
transpose |
Transpose a model |
truncate |
truncate a component fit |