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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
Documentation
- Examples that run
- 100%
- Documented parameters
- 89%
- Return-value docs
- 100%
- References docs
- 50%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
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
- 0.1.1Latest
- 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
- RR 4.0.0 released · 2020-04-24
Package metadata
- First published
- 2020-06-03
- Total releases
- 2 / 6 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.2
- Bundled data
- 152 KB / 2 files
- Download size
- 1.8 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet