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.
Dr. Geoffrey Hubona held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 3 major state universities in the Eastern United States from 1993-2010. In these positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling.