Structural equation modelling (SEM) is a multivariate statistical technique for estimating complex relationships between observed and latent variables. Although numerous SEM packages exist, each of them has limitations. Some packages are not free or open-source; the most popular package not having this disadvantage is $\textbf{lavaan}$, but it is written in R language, which is behind current mainstream tendencies that make it harder to be incorporated into developmental pipelines (i.e. bioinformatical ones). Thus we developed the Python package $\textbf{semopy}$ to satisfy those criteria. The paper provides detailed examples of package usage and explains it's inner clockworks. Moreover, we developed the unique generator of SEM models to extensively test SEM packages and demonstrated that $\textbf{semopy}$ significantly outperforms $\textbf{lavaan}$ in execution time and accuracy.
翻译:结构方程式建模( SEM) 是一种多变量统计技术, 用于估计观测到的变量和潜在变量之间的复杂关系。 虽然存在多个 SEM 套件, 但每个套件都有局限性。 有些套件不是免费或开放源码的, 一些最受欢迎的套件没有这种劣势的套件是$\ textbf{lavaan}$, 但是它用R 语言写成, 这是当前主流趋势的背后, 使得难以将其纳入开发管道( 即生物信息管道) 。 因此我们开发了 Python 套件 $\ textb{smopy} $( $\ textbf{lavaan} $ ), 以满足这些标准。 该文件提供了包使用的详细示例, 并解释了其内部时钟工作 。 此外, 我们开发了独特的 SEM 模型生成器来广泛测试 SEM 套件, 并显示 $\ textb{ sempy} $ 明显低于 $\ textb{lavaan} 执行时间和准确性 。