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).
- Version2.1
- R version≥ 3.5.0 stats,
- LicenseGPL-2
- Needs compilation?No
- Last release01/30/2020
Team
Kaijun Wang
Subhadeep Mukhopadhyay
Insights
Last 30 days
This package has been downloaded 163 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 6 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 2,167 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Sep 11, 2024 with 27 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Depends2 packages
- Reverse Depends1 package
- Reverse Imports1 package