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> |
| 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 |
| 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 |
| 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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