PLS Path Modeling with the semPLS and PLSPM Packages in R

How to make use of the unique semPLS and PLSPM packages features and capabilities to estimate path models.

The course PLS Path Modeling with the semPLS and PLSPM packages in R demonstrates the major capabilities and functions of the R semPLS package; and the major capabilities and functions of the R PLSPM package. Although the semPLS and plspm R packages use the same PLS algorithm as does SmartPLS, and consequently produce identical PLS model estimates (in almost all cases with a few exceptions), each of the two R packages also contains additional, useful, complementary functions and capabilities. Specifically, semPLS has some interesting plots and graphs of PLS path model estimates and also converts your model to run in covariance-based R functions (which is quite handy!). On the other hand, the PLSPM package has very complete and well-formatted PLS output that is consistent with the tables and reports required for publication, and also has very useful and unique multigroup-moderation analysis capabilities, and a unique REBUS-PLS function for discovering heterogeneity (more multi-group differences). If you are interested in knowing a lot about PLS path modeling, it is certainly a good use of your time to become familiar with both the semPLS and PLSPM packages in R.