Composite-Based Structural Equation Modeling

Estimate, assess, test, and study linear, nonlinear, hierarchical and multigroup structural equation models using composite-based approaches and procedures, including estimation techniques such as partial least squares path modeling (PLS-PM) and its derivatives (PLSc, ordPLSc, robustPLSc), generalized structured component analysis (GSCA), generalized structured component analysis with uniqueness terms (GSCAm), generalized canonical correlation analysis (GCCA), principal component analysis (PCA), factor score regression (FSR) using sum score, regression or bartlett scores (including bias correction using Croon’s approach), as well as several tests and typical postestimation procedures (e.g., verify admissibility of the estimates, assess the model fit, test the model fit etc.).


Reference manual

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0.4.0 by Manuel E. Rademaker, 6 months ago,

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Authors: Manuel E. Rademaker [aut, cre] , Florian Schuberth [aut] , Tamara Schamberger [ctb] , Michael Klesel [ctb] , Theo K. Dijkstra [ctb] , Jörg Henseler [ctb]

Documentation:   PDF Manual  

GPL-3 license

Imports abind, alabama, cli, crayon, expm, future.apply, future, lavaan, magrittr, MASS, Matrix, matrixcalc, matrixStats, polycor, progressr, psych, purrr, Rdpack, rlang, stats, symmoments, utils, lifecycle

Suggests dplyr, tidyr, knitr, nnls, prettydoc, plotly, rmarkdown, rootSolve, listviewer, testthat, ggplot2, openxlsx

See at CRAN