{ "_id": "6acd124ba14063b82c3ab0bc", "Package": "plssem", "Type": "Package", "Title": "Complex Partial Least Squares Structural Equation Modeling", "Version": "0.2.0", "Authors@R": "person(given = \"Kjell\", family = \"Solem Slupphaug\",\nemail = \"slupphaugkjell@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0009-0005-8324-2834\"))", "Maintainer": "Kjell Solem Slupphaug ", "Description": "Estimate complex Structural Equation Models (SEMs) by\nfitting Partial Least Squares Structural Equation Modeling\n(PLS-SEM) and Partial Least Squares consistent Structural\nEquation Modeling (PLSc-SEM) specifications that handle\ncategorical data, non-linear relations, and multilevel\nstructures. The implementation follows Lohmöller (1989) for the\nclassic PLS-SEM algorithm, Dijkstra and Henseler (2015) for\nconsistent PLSc-SEM, Dijkstra et al., (2014) for nonlinear\nPLSc-SEM, and Schuberth, Henseler, Dijkstra (2018) for ordinal\nPLS-SEM and PLSc-SEM. Additional extensions are under\ndevelopment. The MC-OrdPLSc algorithm, used to handle ordinal\ninteraction models is detailed in Slupphaug et al., (2026).\nReferences: Lohmöller, J.-B. (1989, ISBN:9783790803002).\n\"Latent Variable Path Modeling with Partial Least Squares.\"\nDijkstra, T. K., & Henseler, J. (2015).\n. \"Consistent partial least\nsquares path modeling.\" Dijkstra, T. K., & Schermelleh-Engel,\nK. (2014). . \"Consistent\npartial least squares for nonlinear structural equation\nmodels.\" Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018).\n. \"Partial least squares path\nmodeling using ordinal categorical indicators.\" Slupphaug, K.\nMehmetoglu, M. & Mittner, M. 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