Package: tensorBSS 0.3.9

tensorBSS: Blind Source Separation Methods for Tensor-Valued Observations

Contains several utility functions for manipulating tensor-valued data (centering, multiplication from a single mode etc.) and the implementations of the following blind source separation methods for tensor-valued data: 'tPCA', 'tFOBI', 'tJADE', k-tJADE', 'tgFOBI', 'tgJADE', 'tSOBI', 'tNSS.SD', 'tNSS.JD', 'tNSS.TD.JD', 'tPP' and 'tTUCKER'.

Authors:Joni Virta [aut, cre], Christoph L. Koesner [aut], Bing Li [aut], Klaus Nordhausen [aut], Hannu Oja [aut], Una Radojicic [aut]

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

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

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • zip.test - Handwritten Digit Recognition Data
  • zip.train - Handwritten Digit Recognition Data

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.41 score 1 stars 26 scripts 289 downloads 1 mentions 31 exports 82 dependencies

Last updated 2 months agofrom:3aec27e719. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 12 2024
R-4.5-win-x86_64OKNov 12 2024
R-4.5-linux-x86_64OKNov 12 2024
R-4.4-win-x86_64OKNov 12 2024
R-4.4-mac-x86_64OKNov 12 2024
R-4.4-mac-aarch64OKNov 12 2024
R-4.3-win-x86_64OKNov 12 2024
R-4.3-mac-x86_64OKNov 12 2024
R-4.3-mac-aarch64OKNov 12 2024

Exports:ggtaugplotggtladleplotk_tJADEmFlattenmModeAutoCovariancemModeCovarianceprint.taugprint.tladleselectComponentstensorBoottensorCenteringtensorStandardizetensorTransformtensorTransform2tensorVectorizetFOBItgFOBItgJADEtJADEtMDtNSS.JDtNSS.SDtNSS.TD.JDtPCAtPCAaugtPCAladletPPtSIRtSOBItTUCKERzip2image

Dependencies:abindbootBSSprepcliclueclustercolorspacecpp11crayoncurlDBIdplyrfansifarverfICAforcatsforecastfracdiffgenericsGGallyggplot2ggstatsgluegtablehmsICSICSNPICtestisobandJADEjsonlitelabelinglatticelifecyclelmtestmagrittrMASSMatrixmgcvminqamitoolsmunsellmvtnormnlmennetnumDerivpatchworkpillarpkgconfigplyrpngprettyunitsprogresspurrrquadprogquantmodR6RColorBrewerRcppRcppArmadilloRcppRollrlangscalesstringistringrsurveysurvivaltensortibbletidyrtidyselecttimeDatetsBSStseriesTTRurcautf8vctrsviridisLitewithrxtszoo

Readme and manuals

Help Manual

Help pageTopics
Blind Source Separation Methods for Tensor-Valued ObservationstensorBSS-package tensorBSS
Augmentation plot for each mode of an object of class taug using ggplot2ggtaugplot
Ladle plot for each mode of an object of class tladle using ggplot2ggtladleplot
k-tJADE for Tensor-Valued Observationsk_tJADE
Flattening an Array Along One ModemFlatten
The m-Mode Autocovariance MatrixmModeAutoCovariance
The m-Mode Covariance MatrixmModeCovariance
Plot an Object of the Class tbssplot.tbss
Printing an object of class taugprint.taug
Printing an object of class tladleprint.tladle
Select the Most Informative ComponentsselectComponents
Bootstrapping or Permuting a Data TensortensorBoot
Center an Array of ObservationstensorCentering
Standardize an Observation ArraytensorStandardize
Linear Transformation of Tensors from mth ModetensorTransform
Linear Transformations of Tensors from Several ModestensorTransform2
Vectorize an Observation TensortensorVectorize
FOBI for Tensor-Valued ObservationstFOBI
gFOBI for Tensor-Valued Time SeriestgFOBI
gJADE for Tensor-Valued Time SeriestgJADE
tJADE for Tensor-Valued ObservationstJADE
Minimum Distance Index of a Kronecker ProducttMD
NSS-JD Method for Tensor-Valued Time SeriestNSS.JD
NSS-SD Method for Tensor-Valued Time SeriestNSS.SD
TNSS-TD-JD Method for Tensor-Valued Time SeriestNSS.TD.JD
PCA for Tensor-Valued ObservationstPCA
Order Determination for Tensorial PCA Using AugmentationtPCAaug
Ladle Estimate for tPCAtPCAladle
Projection pursuit for Tensor-Valued ObservationstPP
SIR for Tensor-Valued ObservationstSIR
SOBI for Tensor-Valued Time SeriestSOBI
Tucker (2) Transformation for a TensortTUCKER
Handwritten Digit Recognition Datazip.test
Handwritten Digit Recognition Datazip.train
function to convert row of zip file to format used by image()zip2image