Package: psychnets Title: Tidy Clean-Room Psychological Network Modeling Version: 0.5.2 Authors@R: c(person("Mohammed", "Saqr", email = "saqr@saqr.me", role = c("aut", "cre", "cph")), person("Sonsoles", "López-Pernas", role = "aut")) Description: 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 . 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. License: GPL-3 URL: https://pak.dynasite.org/psychnets, https://github.com/mohsaqr/psychnets BugReports: https://github.com/mohsaqr/psychnets/issues Encoding: UTF-8 LazyData: true Roxygen: list(markdown = TRUE) RoxygenNote: 7.3.3 Depends: R (>= 3.5.0) Imports: grDevices, graphics, parallel, stats Suggests: cocor, cograph, glasso, glmnet, igraph, IsingFit, knitr, mgm, mvtnorm, networktools, psych, qgraph, rmarkdown, testthat (>= 3.0.0), tna VignetteBuilder: knitr Config/testthat/edition: 3 Repository: https://mohsaqr.r-universe.dev Date/Publication: 2026-10-16 15:00:40 UTC RemoteUrl: https://github.com/mohsaqr/psychnets RemoteRef: HEAD RemoteSha: aa81f15043b66401ab498238490c3d4480914eb5 NeedsCompilation: no Packaged: 2026-10-16 17:40:42 UTC; root Author: Mohammed Saqr [aut, cre, cph], Sonsoles López-Pernas [aut] Maintainer: Mohammed Saqr