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NoLimits.jl: Flexible and Composable Nonlinear Mixed-Effects Modeling in Julia

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arXiv:2606. 24427v1 Announce Type: cross Abstract: Nonlinear mixed-effects models are widely used to analyze longitudinal data, but existing open-source software often supports only a limited subset of the model structures, inference methods, machine-learning components, automatic differentiation techniques, and random-effects distributions required in modern applications.

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Authors: Manuel Huth, Jonas Arruda, Nina Schmid, Roy Gusinow, Vincent Wieland, Clemens Peiter, Jan Hasenauer
Released: 2026-06-24 (15d ago)
Topic: Related to current project
Relation to my work: Read next because NoLimits.jl: Flexible and Composable Nonlinear Mixed-Effects Modeling in Julia overlaps with clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)". Matching terms: soft, source, line, rate, compare, chain, language, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.24427

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