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

semopy 2: A Structural Equation Modeling Package with Random Effects in Python