SEMinR seeks to bring the latest state-of-the-art advances in SEM methods to the R ecosystem. This package also seeks to make describing and analyzing SEMs easier for practitioners.<br><br>There have been several recent advances in the various branches of SEM that are often not reflected in existing R packages. For example, the PLS-PM approach requires adjustment in how models with interaction terms are estimated. PLS-PM methods have recently incorporated predictive methods such as plsPredict. Meanwhile, CB-SEM approach can avail ten Berge factor-score extraction that obtains construct scores with the same correlation patterns as the latent factors themselves. CB-SEM researchers should also consider VIF scores in their regression models. SEMinR incorporates these and other advancements.<br><br>Estimating an SEM using CB-SEM and PLS-PM requires different packages for the two estimation methods, which often requires researchers to wholly redescribe their models in different syntax. SEMinR allows researchers to describe their model once in a common syntax, and estimate the model using different estimation methods. SEMinr includes its own implementation of PLS-PM estimation that is tested against leading commercial applications to ensure comparable results. For CB-SEM estimation, SEMinR delegates the estimation to the popular Lavaan package. Regardless of which estimation method one uses, the results are structured in a similar way for reporting and visualization.<br><br>R packages for SEM often use a custom syntax that does not correspond to any programming language; nor does the syntax not reflect the terminology of SEM with which practitioners are familiar. SEMinR offers researchers a domain-specific language for modeling SEMs that uses function names that evoke major SEM components: constructs, relationships, paths, reflective, composite, etc. As SEMinR’s syntax is built using R functions, researchers can inject their own custom functions to extend the behavior of SEMinR.<br><br>SEMinR is the first package that allows researchers applying PLS-PM to visualize their graphical models and measurement qualities. Visualization of CB-SEM models is delegated to the semplot package. Moreover, SEMinR allows researchers to visualize models either before or after estimation.
|Publikationsstatus||Veröffentlicht - 04.09.2021|