sesem: Spatially explicit structural equation modeling

Structural equation modeling (SEM) is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex datasets with intercorrelated dependent and independent variables. Here we implement a simple method for spatially explicit SEM (SE-SEM) based on the analysis of variance covariance matrices calculated across a range of lag distances. This method provides readily interpretable plots of the change in path coefficients across scale.

Version: 1.0.1
Depends: R (≥ 1.8.0)
Imports: lavaan, mgcv, gplots
Published: 2014-03-04
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>
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_1.0.1.tar.gz
Windows binaries: r-devel: sesem_1.0.1.zip, r-release: sesem_1.0.1.zip, r-oldrel: sesem_1.0.1.zip
OS X Snow Leopard binaries: r-release: sesem_1.0.1.tgz, r-oldrel: sesem_1.0.1.tgz
OS X Mavericks binaries: r-release: sesem_1.0.1.tgz
Old sources: sesem archive