lingamr
0.1.2'LiNGAM' Algorithms for Causal Discovery
Overview
R implementation of 'LiNGAM' (Linear Non-Gaussian Acyclic Model) algorithms for causal discovery, following Shimizu et al. (2011) https://www.jmlr.org/papers/v12/shimizu11a.html. Based on the 'Python' implementation by Ikeuchi et al. (2023) https://github.com/cdt15/lingam. The 'VAR-LiNGAM' residual diagnostics are inspired by the 'VARLiNGAM' R code of Moneta et al. https://sites.google.com/site/dorisentner/publications/VARLiNGAM.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-07-187 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 92%
- Documented parameters
- 100%
- Return-value docs
- 98%
- References docs
- 6%
Downloads
Repository
Repository practices
5 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/morimotoosamu/lingamr on 2026-08-16.
Development tooling
Uses AI-assisted development tooling (declared in repo)
Earliest detected marker: claude on 2026-06-01
Most recent: claude on 2026-06-01
- claude: on or before 2026-06-01 · evidence B, D
Dependencies
Nothing depends on this yet.
Code & Tests
People & History
1 release. R releases are shown for context.
- 0.1.2Latest2026-07-17 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-07-17
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.1.0
- Download size
- 1.0 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
Cite
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