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).

Version: 2.1
Depends: R (≥ 3.5.0), stats, car, PMA
Published: 2020-01-30
Author: Subhadeep Mukhopadhyay, Kaijun Wang
Maintainer: Kaijun Wang <kaijun.wang at temple.edu>
License: GPL-2
NeedsCompilation: no
CRAN checks: LPGraph results

Downloads:

Reference manual: LPGraph.pdf
Package source: LPGraph_2.1.tar.gz
Windows binaries: r-devel: LPGraph_2.1.zip, r-release: LPGraph_2.1.zip, r-oldrel: LPGraph_2.1.zip
macOS binaries: r-release: LPGraph_2.1.tgz, r-oldrel: LPGraph_2.1.tgz
Old sources: LPGraph archive

Reverse dependencies:

Reverse depends: LPKsample
Reverse imports: LPsmooth

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