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0.1.1Graphical Independence Filtering
Overview
Provides a method of recovering the precision matrix for Gaussian graphical models efficiently. Our approach could be divided into three categories. First of all, we use Hard Graphical Thresholding for best subset selection problem of Gaussian graphical model, and the core concept of this method was proposed by Luo et al. (2014) <arXiv:1407.7819>. Secondly, a closed form solution for graphical lasso under acyclic graph structure is implemented in our package (Fattahi and Sojoudi (2019) <https://jmlr.org/papers/v20/17-501.html>). Furthermore, we implement block coordinate descent algorithm to efficiently solve the covariance selection problem (Dempster (1972) <doi:10.2307/2528966>). Our package is computationally efficient and can solve ultra-high-dimensional problems, e.g. p > 10,000, in a few minutes.
Install
Health
- NOTE r-devel-linux-x86_64-debian-clang
- NOTE r-devel-linux-x86_64-debian-gcc
- NOTE2026-04-2210 OK · 4 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-189 OK · 4 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-03-1010 OK · 4 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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Code
Code & tests
Open call graph →Line coverage
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Tests / Examples
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Functions
21 3 exported
Complexity
3.4 avg / 9 max
Call network
21 nodes / 5 edges
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People & History
2 releases. Pick two to compare their code metrics; R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 0.1.1Latest2024-01-12 · current release
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 0.1.02020-06-03
Package metadata
- First published
- 2020-06-03
- Total releases
- 2 / 6 yrs
- License
- GPL (>= 2) OSI
- Download size
- not tracked yet
- Installed size
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- With dependencies
- not tracked yet