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English(EN) NoLimits.jl: Flexible and Composable Nonlinear Mixed-Effects Modeling in Julia

Julia 发布 NoLimits.jl，用于灵活的非线性混合效应建模

作者 PulseAugur 编辑部 · [2 个来源] ·

一款名为 NoLimits.jl 的新的开源 Julia 包已发布，旨在提供灵活且可组合的非线性混合效应建模。该包通过支持更广泛的模型结构、推理方法、机器学习组件和随机效应分布，解决了现有软件的局限性。NoLimits.jl 利用基于宏的语言，从各种构建块（包括微分方程和神经网络）构建模型，并集成了多种频率学派和贝叶斯学派的推理技术。 AI

影响 增强了统计建模的能力，可能影响依赖复杂纵向数据分析的 AI 研究。

排序理由 该集群包含一篇描述新的统计建模软件包的学术论文。

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

Julia 发布 NoLimits.jl，用于灵活的非线性混合效应建模

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报道来源 [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：Julia 中灵活且可组合的非线性混合效应建模

    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：Julia 中灵活且可组合的非线性混合效应建模

    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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