Structural equation modelling in R: Comparing

lavaan

and

seminr

packages

This paper compares two popular R packages for structural equation modelling (SEM) –

lavaan

and

seminr

– to help researchers understand not only how they work, but also when each is most appropriate. Although both tools allow users to estimate the same structural models, they are grounded in different methodological traditions and are designed to support different research goals. Using an identical model, we estimated results with both covariance-based SEM (

lavaan

) and variance-based SEM (

seminr

) and compared their outputs, including model specification syntax, evaluation criteria, and reporting conventions. The results show that both approaches lead to substantively similar conclusions regarding the relationships between constructs, while differing in emphasis:

lavaan

provides richer global model-fit diagnostics, whereas

seminr

places greater emphasis on prediction-oriented assessment and convenient access to latent variable scores. The contribution of this study lies in its practical, hands-on demonstration rather than in a theoretical or simulation-based comparison. The findings reinforce that there is no universally “better” SEM approach; instead, methodological choice should be guided by the research objective. Researchers focussed on theory testing may benefit more from

lavaan

, while those prioritising prediction or exploratory analysis may find

seminr

more suitable. Ultimately, considering both perspectives can support more transparent, robust, and methodologically appropriate SEM applications.