cSEM: 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.).

Version:	0.2.0
Depends:	R (≥ 3.5.0)
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
Published:	2020-03-29
Author:	Manuel E. Rademaker [aut, cre], Florian Schuberth [aut], Tamara Schamberger [ctb], Michael Klesel [ctb], Theo K. Dijkstra [ctb], Jörg Henseler [ctb]
Maintainer:	Manuel E. Rademaker <manuel.rademaker at uni-wuerzburg.de>
BugReports:	https://github.com/M-E-Rademaker/cSEM/issues
License:	GPL-3
URL:	https://github.com/M-E-Rademaker/cSEM, https://m-e-rademaker.github.io/cSEM/
NeedsCompilation:	no
Citation:	cSEM citation info
Materials:	README
CRAN checks:	cSEM results

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