spectralGraphTopology
0.2.3Learning Graphs from Data via Spectral Constraints
Overview
In the era of big data and hyperconnectivity, learning high-dimensional structures such as graphs from data has become a prominent task in machine learning and has found applications in many fields such as finance, health care, and networks. 'spectralGraphTopology' is an open source, documented, and well-tested R package for learning graphs from data. It provides implementations of state of the art algorithms such as Combinatorial Graph Laplacian Learning (CGL), Spectral Graph Learning (SGL), Graph Estimation based on Majorization-Minimization (GLE-MM), and Graph Estimation based on Alternating Direction Method of Multipliers (GLE-ADMM). In addition, graph learning has been widely employed for clustering, where specific algorithms are available in the literature. To this end, we provide an implementation of the Constrained Laplacian Rank (CLR) algorithm.
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6 releases. R releases are shown for context.
- RR 4.5.0 released · 2025-04-11
- archivedRemoved from CRAN2025-02-28issues were not corrected despite reminders
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- 0.2.32022-03-14 · diff ↗
- 0.2.22022-03-11 · diff ↗
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- 0.2.02019-10-12 · diff ↗
- unarchivedReturned to CRAN2019-10-12
- 0.1.22019-09-22 · diff ↗
- unarchivedReturned to CRAN2019-09-22Re-archived 2019-09-24 as check problems were still present
- archivedRemoved from CRAN2019-09-22check problems were not corrected in time
- 0.1.12019-06-03 · diff ↗
- 0.1.02019-05-08
Show 1 earlier events
- RR 3.6.0 released · 2019-04-26
Package metadata
- Total releases
- 6
- License
- GPL-3 OSI
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- not tracked yet
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