SEMinR: Domain-specific language for building, estimating, and visualizing structural equation models in R
Citation:
Ray, S., Danks, N.P., and Calero Valdez, A., SEMinR: Domain-specific language for building, estimating, and visualizing structural equation models in R, V2.3.1, CRAN, The Comprehensive R Archive Network, 2021Download Item:
Abstract:
SEMinR allows researchers to easily create, estimate, and visualize structural equation models (SEMs) for
multiple estimation methods. SEMs are popular modeling techniques in social sciences and the life sciences,
and can estimate relationships between concepts that need to be measured by multiple items. SEMinR can
estimate SEMs using either covariance-based SEM (CBSEM, such as found in LISREL and Lavaan), or Partial
Least Squares Path Modeling (PLS-PM, such as found in SmartPLS, semPLS, plspm, and csem). Moreover,
SEMinR implements several advances in SEM methodologies not found elsewhere. And it also allows for
visualization of all kinds of SEM models. SEMinR’s model description syntax is plain-old-R-functions
(PORF!), which allows users to extend and compose syntax in novel ways. Thus, SEMinR is a one-stop-shop
for both SEM practitioners seeking to analyze empirical models and SEM methodologists seeking to automate
and extend SEM methods. SEMinR is increasingly being used in universities, for both research and teaching
needs, and companies world-wide.
URI:
https://cran.r-project.org/web/packages/seminr/index.htmlhttps://github.com/sem-in-r/seminr
https://www.facebook.com/groups/seminr
http://hdl.handle.net/2262/101089
Author's Homepage:
http://people.tcd.ie/danksn
Author: Danks, Nicholas
Publisher:
The Comprehensive R Archive NetworkType of material:
SoftwareURI:
https://cran.r-project.org/web/packages/seminr/index.htmlhttps://github.com/sem-in-r/seminr
https://www.facebook.com/groups/seminr
http://hdl.handle.net/2262/101089
Series/Report no:
Y;Availability:
Full text availableSubject (TCD):
Digital Engagement , Digital Humanities , Inclusive Society , ALGORITHM , Quantitative Research , open source , partial least squaresEdition:
V2.3.1Metadata
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