Package: multivarious 0.2.0

multivarious: Extensible Data Structures for Multivariate Analysis

Provides a set of basic and extensible data structures and functions for multivariate analysis, including dimensionality reduction techniques, projection methods, and preprocessing functions. The aim of this package is to offer a flexible and user-friendly framework for multivariate analysis that can be easily extended for custom requirements and specific data analysis tasks.

Authors:Bradley Buchsbaum [aut, cre]

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multivarious.pdf |multivarious.html✨
multivarious/json (API)

# Install 'multivarious' in R:
install.packages('multivarious', repos = c('https://bbuchsbaum.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/bbuchsbaum/multivarious/issues

Pkgdown site:https://bbuchsbaum.github.io

On CRAN:

3.53 score 17 scripts 225 downloads 61 exports 28 dependencies

Last updated 2 months agofrom:6dbcc6ed81. Checks:8 ERROR. Indexed: yes.

TargetResultLatest binary
Doc / VignettesFAILFeb 16 2025
R-4.5-winERRORFeb 16 2025
R-4.5-macERRORFeb 16 2025
R-4.5-linuxERRORFeb 16 2025
R-4.4-winERRORFeb 16 2025
R-4.4-macERRORFeb 16 2025
R-4.3-winERRORFeb 16 2025
R-4.3-macERRORFeb 16 2025

Exports:add_nodeapply_rotationapply_transformbi_projectorbi_projector_unionblock_indicesblock_lengthsbootstrapcenterclassifiercolscalecomponentscompose_partial_projectorcompose_projectorconcat_pre_processorsconvert_domaincPCAcross_projectordiscriminant_projectorfeature_importancefreshgeneiggroup_meansinit_transforminverse_projectionmultiblock_biprojectormultiblock_projectornblocksncompnystrom_approxpartial_inverse_projectionpartial_projectpartial_projectorpasspcaperm_ciprepprinangprojectproject_blockproject_varsprojectorrank_scorereconstructrefitregressrelative_eigenreprocessresidualizeresidualsreverse_transformrf_classifierrotatescoressdevshapestandardizestd_scoressvd_wrappertransposetruncate

Dependencies:chkclicodetoolscorpcorfitdistrplusforeachglmnetglueirlbaiteratorslatticelifecyclemagrittrMASSMatrixmatrixStatsplsproxypurrrRcppRcppEigenrlangRSpectrarsvdshapesurvivalsvdvctrs

Readme and manuals

Help Manual

Help pageTopics
add a pre-processing stageadd_node
Add a pre-processing node to a pipelineadd_node.prepper
Apply rotationapply_rotation
apply a pre-processing transformapply_transform
Construct a bi_projector instancebi_projector
A Union of Concatenated 'bi_projector' Fitsbi_projector_union
get block_indicesblock_indices
Extract the Block Indices from a Multiblock Projectorblock_indices.multiblock_projector
get block_lengthsblock_lengths
Bootstrap Resampling for Multivariate Modelsbootstrap
PCA Bootstrap Resamplingbootstrap.pca
center a data matrixcenter
Construct a Classifierclassifier
Create a k-NN classifier for a discriminant projectorclassifier.discriminant_projector
Multiblock Bi-Projector Classifierclassifier.multiblock_biprojector
create classifier from a projectorclassifier.projector
Extract coefficients from a cross_projector objectcoef.cross_projector
Coefficients for a Multiblock Projectorcoef.multiblock_projector
scale a data matrixcolscale
get the componentscomponents
Compose Multiple Partial Projectorscompose_partial_projector
Compose Two Projectorscompose_projector
bind together blockwise pre-processorsconcat_pre_processors
Transfer data from one input domain to another via common latent spaceconvert_domain
Contrastive PCA (cPCA) with Adaptive Computation MethodscPCA
Two-way (cross) projection to latent componentscross_projector
Construct a Discriminant Projectordiscriminant_projector
Evaluate feature importancefeature_importance
Evaluate Feature Importancefeature_importance.classifier
Get a fresh pre-processing node cleared of any cached datafresh
Create a fresh pipeline from an existing prepperfresh.prepper
Generalized Eigenvalue Decompositiongeneig
Compute column-wise mean in X for each factor level of Ygroup_means
Inverse of the Component Matrixinverse_projection
is it orthogonalis_orthogonal
Create a Multiblock Bi-Projectormultiblock_biprojector
Create a Multiblock Projectormultiblock_projector
get the number of blocksnblocks
Get the number of componentsncomp
Nyström approximation for kernel-based decomposition (Unified Version)nystrom_approx
Partial Inverse Projection of a Columnwise Subset of Component Matrixpartial_inverse_projection
Partially project a new sample onto subspacepartial_project
Partial Project Through a Composed Partial Projectorpartial_project.composed_partial_projector
Construct a partial projectorpartial_projector
construct a partial_projector from a 'projector' instancepartial_projector.projector
a no-op pre-processing steppass
Principal Components Analysis (PCA)pca
Permutation Confidence Intervalsperm_ci
Permutation-Based Confidence Intervals for PCA Componentsperm_ci.pca
predict with a classifier objectpredict.classifier
prepare a dataset by applying a pre-processing pipelineprep
finalize a prepper pipelineprep.prepper
Compute principal angles for a set of subspacesprinang
Pretty Print S3 Method for bi_projector Classprint.bi_projector
Pretty Print S3 Method for bi_projector_union Classprint.bi_projector_union
Pretty Print Method for 'classifier' Objectsprint.classifier
Print a concat_pre_processor objectprint.concat_pre_processor
Pretty Print Method for 'multiblock_biprojector' Objectsprint.multiblock_biprojector
Print a pre_processor objectprint.pre_processor
Print a prepper pipelineprint.prepper
Pretty Print Method for 'projector' Objectsprint.projector
Pretty Print Method for 'regress' Objectsprint.regress
New sample projectionproject
Project a single "block" of data onto the subspaceproject_block
Project Data onto a Specific Blockproject_block.multiblock_projector
Project one or more variables onto a subspaceproject_vars
project a cross_projector instanceproject.cross_projector
Construct a 'projector' instanceprojector
Calculate Rank Score for Predictionsrank_score
Reconstruct the datareconstruct
refit a modelrefit
Multi-output linear regressionregress
Relative Eigenanalysis with Ecosystem Integrationrelative_eigen
apply pre-processing parameters to a new data matrixreprocess
reprocess a cross_projector instancereprocess.cross_projector
Compute a regression model for each column in a matrix and return residual matrixresidualize
Obtain residuals of a component model fitresiduals
reverse a pre-processing transformreverse_transform
construct a random forest wrapper classifierrf_classifier
Create a random forest classifierrf_classifier.projector
Rotate a Component Solutionrotate
Rotate PCA Loadingsrotate.pca
Retrieve the component scoresscores
standard deviationssdev
Shape of the Projectorshape
shape of a cross_projector instanceshape.cross_projector
center and scale each vector of a matrixstandardize
Compute standardized component scoresstd_scores
Singular Value Decomposition (SVD) Wrappersvd_wrapper
Transpose a modeltranspose
truncate a component fittruncate