Package: LPGraph 2.1

Kaijun Wang

LPGraph: Nonparametric Smoothing of Laplacian Graph Spectra

A nonparametric method to approximate Laplacian graph spectra of a network with ordered vertices. This provides a computationally efficient algorithm for obtaining an accurate and smooth estimate of the graph Laplacian basis. The approximation results can then be used for tasks like change point detection, k-sample testing, and so on. The primary reference is Mukhopadhyay, S. and Wang, K. (2018, Technical Report).

Authors:Subhadeep Mukhopadhyay, Kaijun Wang

LPGraph_2.1.tar.gz
LPGraph_2.1.zip(r-4.7)LPGraph_2.1.zip(r-4.6)LPGraph_2.1.zip(r-4.5)
LPGraph_2.1.tgz(r-4.6-any)LPGraph_2.1.tgz(r-4.5-any)
LPGraph_2.1.tar.gz(r-4.7-any)LPGraph_2.1.tar.gz(r-4.6-any)
LPGraph_2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
LPGraph/json (API)

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

Bug tracker:https://github.com/cranhaven/cranhaven.r-universe.dev/issues

Datasets:

On CRAN:

Conda:

archivedpackagesr-universe

2.48 score 5 stars 2 packages 222 downloads 5 exports 70 dependencies

Last updated from:38d104e6ac (on package/LPGraph). Checks:9 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK129
source / vignettesOK189
linux-release-x86_64OK121
macos-release-arm64OK105
macos-oldrel-arm64OK116
windows-develOK69
windows-releaseOK78
windows-oldrelOK86
wasm-releaseOK110

Exports:LaplacianLP.basisLP.struct.testLPSpectralwt.mean

Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecowplotcpp11DerivdoBydplyrfarverforecastFormulafracdiffgenericsggplot2gluegtableisobandlabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigPMApurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangS7scalesSparseMstringistringrsurvivaltibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo