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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 |
| 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 (arm64): LPGraph_2.1.tgz, r-oldrel (arm64): LPGraph_2.1.tgz, r-release (x86_64): LPGraph_2.1.tgz, r-oldrel (x86_64): LPGraph_2.1.tgz |
| Old sources: | LPGraph archive |
| Reverse depends: | LPKsample |
| Reverse imports: | LPsmooth |
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