Non-regularized graphical model methods are specifically designed for low-dimensional data common in the social-behavioral sciences¶
Claim · All claims
Evidence · 1 study · designr1·q1
Currency · newest study 2021 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q1 i? GGMnonreg provides graphical modeling methods purpose-built for low-dimensional (p < n) data, as typical in psychology and the social-behavioral sciences. → Donald Williams 2021
Evidence¶
Donald Williams 2021¶
Donald Williams. (2021). GGMnonreg: Non-regularized Gaussian graphical models in R. Journal of Open Source Software, 6(67), 3308. https://doi.org/10.21105/joss.03308
q1 · i? · design · r1
The article describes the package's purpose: methods "specifically designed for low-dimensional data", motivated by the observation that in psychology "the data is typically long or low-dimensional (p < n". This is a design statement about the artifact, not a comparative performance result.
"The primary purpose of GGMnonreg is to provide methods that were specifically designed for low-dimensional data (e.g., those common in the social-behavioral sciences)."
Discussion¶
Related Claims¶
- Regularization-based graphical modeling has key drawbacks, including difficulty obtaining valid parameter uncertainty and inflated false positive rates — related
- The frequentist graphical LASSO has difficulty estimating centrality indices and uncertainty in these measures in symptom networks — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
ggmnonreg-designed-for-low-dimensional-data |
title |
Non-regularized graphical model methods are specifically designed for low-dimensional data common in the social-behavioral sciences |
description |
Non-regularized graphical model methods are specifically designed for low-dimensional data common in the social-behavioral sciences |
status |
review |
evidence_strength |
weak |
newest_evidence |
2021 |
generated |
by process:wiki-ingest on 2026-10-08 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — donald-williams-2021 |
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