Tidy Clean-Room Psychological Network Modeling R package psychnets version 0.4.3 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. Software 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 Mohammed Saqr <saqr@saqr.me> Comprehensive R Archive Network (CRAN) Mohammed Saqr [aut, cre, cph], Sonsoles López-Pernas [aut] GPL-3 2026-07-30 application/tgz https://CRAN.R-project.org/package=psychnets doi:10.32614/CRAN.package.psychnets