https://agallinat.r-universe.devRecent package updates in agallinatR-universehttps://github.com/agallinat.pnghttps://agallinat.r-universe.devMon, 05 Oct 2026 18:54:06 GMTalex.gaoc@gmail.com (Alex Gallinat)A collection of tools for analyzing and visualizing networks. Summarizes global topology and per-node centrality, identifies hubs, bottlenecks and community structure, and classifies nodes into connectivity roles following Guimera and Amaral (2005) <doi:10.1038/nature03288>. Extracts interpretable subnetworks around a set of nodes, including approximate Steiner trees after Kou, Markowsky and Berman (1981) <doi:10.1007/BF00288961>. Propagates influence from seed nodes using Laplacian smoothing, heat-kernel and random-walk-with-restart diffusion, with degree-matched permutation nulls, and simulates network robustness under progressive node removal. Observed metrics can be tested against degree-preserving random ensembles. Edge weights are supported throughout, with an explicit declaration of whether a weight means strength or distance. Matrix operations use sparse representations throughout so that the methods remain usable on large graphs.https://github.com/r-universe/agallinat/actions/runs/37592829344Mon, 05 Oct 2026 18:54:06 GMTnetkit0.1.0successhttps://agallinat.r-universe.devhttps://github.com/agallinat/netkitintroduction.Rmdintroduction.htmlIntroduction to netkit2025-07-24 19:25:452026-10-05 18:31:00weighted-networks.Rmdweighted-networks.htmlWeighted networks: strength or distance?2026-10-03 17:59:182026-10-03 17:59:18baloo.142@gmail.com (Alex Gallinat)Replaces the contents of a sensitive or proprietary data file with synthetic values while keeping the columns, classes, formats and file type identical, so that analysis code written against the anonymised copy runs unchanged on the real data. Intended for sharing the structure of a dataset with a collaborator or with an AI assistant (a large language model or LLM chatbot) without sharing its contents. Reads and writes delimited text, 'Excel', 'SPSS', 'Stata', 'RDS' and 'Parquet' files, and includes a local 'Shiny' app and a command line script. Reduces disclosure risk; it offers no formal privacy guarantee.https://github.com/r-universe/agallinat/actions/runs/37592833100Fri, 02 Oct 2026 21:41:50 GMTdatablinder0.1.0successhttps://agallinat.r-universe.devhttps://github.com/agallinat/datablinderalternatives.Rmdalternatives.htmlOther packages for fake and anonymised data2026-09-27 01:14:402026-09-29 15:02:40sharing-data-with-ai.Rmdsharing-data-with-ai.htmlSharing sensitive data with an AI assistant2026-09-27 01:14:402026-09-29 15:02:40limits.Rmdlimits.htmlWhat datablinder does not protect you from2026-09-27 01:14:402026-09-29 15:02:40