sesem: Spatially Explicit Structural Equation Modeling

Structural equation modeling is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with inter-correlated dependent and independent variables. Here we implement a simple method for spatially explicit structural equation modeling based on the analysis of variance co-variance matrices calculated across a range of lag distances. This method provides readily interpreted plots of the change in path coefficients across scale.

Version: 1.0.2
Depends: R (≥ 1.8.0)
Imports: lavaan, mgcv, gplots
Published: 2016-06-10
Author: Eric Lamb [aut, cre], Kerrie Mengersen [aut], Katherine Stewart [aut], Udayanga Attanayake [aut], Steven Siciliano [aut]
Maintainer: Eric Lamb <eric.lamb at usask.ca>
BugReports: NA
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://www.r-project.org, http://homepage.usask.ca/~egl388/index.html
NeedsCompilation: no
Citation: sesem citation info
Materials: NEWS
CRAN checks: sesem results

Downloads:

Reference manual: sesem.pdf
Package source: sesem_1.0.2.tar.gz
Windows binaries: r-devel: sesem_1.0.2.zip, r-release: sesem_1.0.2.zip, r-oldrel: sesem_1.0.2.zip
OS X Mavericks binaries: r-release: sesem_1.0.2.tgz, r-oldrel: sesem_1.0.2.tgz
Old sources: sesem archive