LPGraph v2.1
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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).
Functions in LPGraph
| Name | Description | |
| LPSpectral | Nonparametric smooth approximation of the Laplacian graph spectra | |
| LPGraph-package | LPGraph | |
| senate | Senate Vote Data | |
| LP.basis | Computes LP basis function of a discrete distribution | |
| LP.struct.test | Detection of structures in an ordered-network. | |
| No Results! | ||
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Details
| Type | Package |
| Date | 2020-01-29 |
| License | GPL-2 |
| NeedsCompilation | no |
| Packaged | 2020-01-30 05:34:33 UTC; AquinasUnit |
| Repository | CRAN |
| Date/Publication | 2020-01-30 20:20:02 UTC |
| depends | car , PMA , R (>= 3.5.0) , stats |
| Contributors | Subhadeep Mukhopadhyay, Kaijun Wang |
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