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]

multivarious_0.2.0.tar.gz
multivarious_0.2.0.zip(r-4.5)multivarious_0.2.0.zip(r-4.4)multivarious_0.2.0.zip(r-4.3)
multivarious_0.2.0.tgz(r-4.4-any)multivarious_0.2.0.tgz(r-4.3-any)
multivarious_0.2.0.tar.gz(r-4.5-noble)multivarious_0.2.0.tar.gz(r-4.4-noble)
multivarious_0.2.0.tgz(r-4.4-emscripten)multivarious_0.2.0.tgz(r-4.3-emscripten)
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'))

Peer review:

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

On CRAN:

4.40 score 10 scripts 155 downloads 56 exports 28 dependencies

Last updated 6 months agofrom:93f7dc63f2. Checks:OK: 1 ERROR: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 10 2024
R-4.5-winERRORNov 10 2024
R-4.5-linuxERRORNov 10 2024
R-4.4-winERRORNov 10 2024
R-4.4-macERRORNov 10 2024
R-4.3-winERRORNov 10 2024
R-4.3-macERRORNov 10 2024

Exports:add_nodeapply_rotationapply_transformbi_projectorbi_projector_unionblock_indicesblock_lengthsbootstrapcenterclassifiercolscalecomponentscompose_projectorcompose_projectorsconcat_pre_processorsconvert_domaincross_projectordiscriminant_projectorfreshgroup_meansinit_transforminverse_projectionmultiblock_biprojectormultiblock_projectornblocksncompnystrom_embeddingpartial_inverse_projectionpartial_projectpartial_projectorpasspcaperm_ciprepprinangprojectproject_blockproject_varsprojectorreconstructrefitregressreprocessresidualizeresidualsreverse_transformrf_classifierrotatescoressdevshapestandardizestd_scoressvd_wrappertransposetruncate

Dependencies:chkclicodetoolscorpcorfitdistrplusforeachglmnetglueirlbaiteratorslatticelifecyclemagrittrMASSMatrixmatrixStatsplsproxypurrrRcppRcppEigenrlangRSpectrarsvdshapesurvivalsvdvctrs

Introduction to the multivarious Package

Rendered fromIntroduction.Rmdusingknitr::rmarkdownon Nov 10 2024.

Last update: 2024-01-12
Started: 2023-05-01

Readme and manuals

Help Manual

Help pageTopics
add a pre-processing stageadd_node
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
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 'projector'classifier.projector
Extract coefficients from a cross_projector objectcoef.cross_projector
scale a data matrixcolscale
get the componentscomponents
Compose Two Projectorscompose_projector
Projector Compositioncompose_projectors
bind together blockwise pre-processorsconcat_pre_processors
Transfer data from one input domain to another via common latent spaceconvert_domain
Two-way (cross) projection to latent componentscross_projector
Construct a Discriminant Projectordiscriminant_projector
Get a fresh pre-processing node cleared of any cached datafresh
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
Nystrom method for out-of-sample embeddingnystrom_embedding
Partial Inverse Projection of a Columnwise Subset of Component Matrixpartial_inverse_projection
Partially project a new sample onto subspacepartial_project
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
predict with a classifier objectpredict.classifier
prepare a dataset by applying a pre-processing pipelineprep
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
Pretty Print Method for 'composed_projector' Objectsprint.composed_projector
Pretty Print Method for 'multiblock_biprojector' Objectsprint.multiblock_biprojector
Pretty Print Method for 'projector' Objectsprint.projector
New sample projectionproject
Project a single "block" of data onto the subspaceproject_block
Project one or more variables onto a subspaceproject_vars
project a cross_projector instanceproject.cross_projector
Construct a 'projector' instanceprojector
Reconstruct the datareconstruct
refit a modelrefit
Multi-output linear regressionregress
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
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