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
DOI: 10.32614/CRAN.package.sesem
Author: Eric Lamb [aut, cre], Kerrie Mengersen [aut], Katherine Stewart [aut], Udayanga Attanayake [aut], Steven Siciliano [aut]
Maintainer: Eric Lamb <eric.lamb at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: sesem citation info
Materials: NEWS
CRAN checks: sesem results


Reference manual: sesem.pdf


Package source: sesem_1.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): sesem_1.0.2.tgz, r-oldrel (arm64): sesem_1.0.2.tgz, r-release (x86_64): sesem_1.0.2.tgz, r-oldrel (x86_64): sesem_1.0.2.tgz
Old sources: sesem archive


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