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NoLimits.jl released for flexible nonlinear mixed-effects modeling in Julia

By PulseAugur Editorial · [2 sources] ·

A new open-source Julia package called NoLimits.jl has been released, designed to offer flexible and composable nonlinear mixed-effects modeling. This package addresses limitations in existing software by supporting a wider range of model structures, inference methods, machine-learning components, and random-effects distributions. NoLimits.jl utilizes a macro-based language to construct models from various building blocks, including differential equations and neural networks, and integrates multiple inference techniques for both frequentist and Bayesian approaches. AI

IMPACT Enhances capabilities for statistical modeling, potentially impacting AI research that relies on complex longitudinal data analysis.

RANK_REASON The cluster contains an academic paper describing a new software package for statistical modeling.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

NoLimits.jl released for flexible nonlinear mixed-effects modeling in Julia

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The cluster contains an academic paper describing a new software package for statistical modeling.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Manuel Huth, Jonas Arruda, Nina Schmid, Roy Gusinow, Vincent Wieland, Clemens Peiter, Jan Hasenauer ·

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

    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…

  2. arXiv stat.ML TIER_1 English(EN) · Jan Hasenauer ·

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

    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 di…

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