psychnets
0.4.3Tidy Clean-Room Psychological Network Modeling
Overview
Provides clean-room implementations for estimating psychometric network models, including correlation and partial-correlation networks, Gaussian graphical models with extended Bayesian information criterion (EBIC) regularization, nonparanormal and stepwise selection variants, information-filtering networks (the triangulated maximally filtered graph and the local-global inverse covariance), relative-importance networks, and Ising and mixed graphical models doi:10.3758/s13428-017-0862-1 doi:10.1007/978-3-031-54464-4_19. All methods are implemented from first principles in base R without compiled dependencies and return consistent, tidy outputs. Functions are designed to be transparent and report optimization diagnostics where applicable. For Gaussian graphical models, the graphical lasso stationarity (Karush-Kuhn-Tucker) residual quantifies the deviation of the estimated solution from the optimum of the corresponding convex optimization problem.
Install
Health
- NOTE r-devel-linux-x86_64-debian-gcc
- NOTE2026-08-019 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-07-317 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 98%
- Return-value docs
- 100%
- References docs
- 8%
Downloads
Repository
Repository practices
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Checks run against github.com/mohsaqr/psychnets on 2026-08-03.
Dependencies
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Code & Tests
Datasets
People & History
1 release. R releases are shown for context.
- 0.4.3Latest2026-07-30 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-07-30
- Total releases
- 1 / 1 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 8.5 KB / 5 files
- Download size
- not tracked yet
- Installed size
- not tracked yet
- With dependencies
- not tracked yet