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  "Description": "Tools for fitting, diagnosing, and analyzing tie-oriented\nand actor-oriented relational event models, under both\nfrequentist and Bayesian approaches. The package supports\ntie-oriented modeling (Butts, 2008,\n<doi:10.1111/j.1467-9531.2008.00203.x>) and an actor-oriented\nmodeling framework (Stadtfeld et al., 2017,\n<doi:10.15195/v4.a14>), with additional model diagnostics and\ngoodness-of-fit tools. Interfaces to estimation backends\nprovide a range of extensions: random-effects (frailty)\nrelational event models capturing sender, receiver, and dyadic\nheterogeneity (Juozaitiene & Wit 2024,\n<doi:10.1007/s11336-024-09952-x>; Mulder & Hoff, 2024,\n<doi:10.1214/24-AOAS1885>), finite mixture and dyadic latent\nclass models for unobserved dyadic heterogeneity (Lakdawala et\nal., 2026, <doi:10.1016/j.socnet.2026.06.006>), penalized\nestimation via the lasso, ridge, and elastic net (Tibshirani,\nR., 1996, <doi:10.1111/j.2517-6161.1996.tb02080.x>; Karimova et\nal., 2023, <doi:10.1016/j.socnet.2023.02.006>), and approximate\nBayesian regularization (Karimova et al., 2025,\n<doi:10.1016/j.jmp.2025.102925>). Modeling of events with a\nduration is also supported (Lakdawala et al., 2026,\n<doi:10.48550/arXiv.2602.21000>) and moving window relational\nevent models (Mulder & Leenders, 2019,\n<doi:10.1016/j.chaos.2018.11.027>; Meijerink et al., 2023,\n<doi:10.1371/journal.pone.0272309>).",
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