2026-08-13 –, Room 2
Structural Equation Models (SEMs) are a powerful statistical framework for disentangling complex relationships between multiple factors that affect empirical data. StructuralEquationModels.jl implements SEMs in Julia, offering greater efficiency and extensibility compared to implementations in other languages. Improved efficiency enables the application of SEM to large networks of variables (such as in genetics) and to fit many models in parallel (such as in neuroimaging). Improved extensibility permits adapting SEMs to specific use cases by modifying loss functions or integrating with various numerical optimization backends.
Structural Equation Models (SEMs) are a class of statistical models applicable to a wide range of modeling tasks, such as time-series analysis, psychometric questionnaires, mediation models, multiple regression models, and multilevel models. This leads to applications across diverse scientific disciplines, including psychology, genetic epidemiology, social science, and neuroscience.
StructuralEquationModels.jl implements SEMs leveraging the Julia package ecosystem, including advanced numerical optimization algorithms, symbolic algebra systems, and support for sparse matrices. This results in two primary benefits over existing open-source implementations in other languages: efficiency and extensibility.
Efficiency refers to the significantly reduced computation time required to estimate SEM parameters compared to other SEM software packages. Improved efficiency allows using SEM for large networks of variables (such as in genetics) or for fitting many models in parallel (such as in neuroimaging or high-throughput experiments in general). Extensibility is achieved through a high degree of composability enabled by multiple dispatch. It allows users to easily add new features to the software to adapt SEMs to specific use cases, such as employing specialized numerical optimization algorithms for regularized estimation of high-dimensional problems.
Together, these features make StructuralEquationModels.jl a valuable tool for rapid prototyping, integration with diverse optimization backends, analysis of large networks of thousands of variables, and high-performance computing applications involving millions of models.
This presentation will
- introduce the fundamentals of structural equation modeling and present selected use cases,
- show how to use StructuralEquationModels.jl to specify models and analyze data,
- explain how the package leverages the Julia ecosystem for efficiency and extensibility,
- compare its computational efficiency to other SEM implementations,
- and provide an outlook on planned additions to the package.
PhD student at the Center for Lifespan Psychology, Max-Planck-Institute for Human Development, Berlin