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.).


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("cSEM")

0.2.0 by Manuel E. Rademaker, 15 hours ago


https://github.com/M-E-Rademaker/cSEM, https://m-e-rademaker.github.io/cSEM/


Report a bug at https://github.com/M-E-Rademaker/cSEM/issues


Browse source code at https://github.com/cran/cSEM


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, psych, purrr, Rdpack, stats, symmoments, utils

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


See at CRAN