Structural Equation Modeling (SEM) is an umbrella term that includes numerous multivariate statistical techniques that are employed throughout a plethora of research areas, ranging from social to natural sciences. Until recently, SEM software was either commercial or restricted to niche languages, and the lack of SEM packages compatible with more mainstream programming languages was dire. To combat that, we introduced a Python package semopy 1 that surpassed other state-of-the-art software in terms of performance and estimation accuracy. Yet, it was lacking in functionality and its usage was burdened with unnecessary boilerplate code. Here, we introduce a complete overhaul of semopy that improves upon the previous results and comes with lots of new capabilities. Furthermore, we propose a novel SEM model that combines in itself a notion of random effects from linear mixed models (LMMs) to model numerous phenomena, such as spatial data, time series or population stratification in genetics.

中文翻译：

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semopy 2：在 Python 中具有随机效应的结构方程建模包

结构方程模型 (SEM) 是一个总括性术语，其中包括许多多元统计技术，这些技术应用于从社会科学到自然科学的众多研究领域。直到最近，SEM 软件要么是商业化的，要么仅限于小众语言，并且缺乏与更多主流编程语言兼容的 SEM 包是可怕的。为了解决这个问题，我们引入了一个 Python 包 semopy 1，它在性能和估计精度方面超越了其他最先进的软件。然而，它缺乏功能，并且它的使用负担了不必要的样板代码。在这里，我们引入了对 semopy 的全面改革，它改进了之前的结果并带来了许多新功能。此外，