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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.

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conda 178.4 kB | noarch/r-sesem-1.0.2-r43h142f84f_0.tar.bz2  1 year and 2 months ago 24 main
conda 178.5 kB | noarch/r-sesem-1.0.2-r42h142f84f_0.tar.bz2  2 years and 9 months ago 53 main
conda 178.0 kB | noarch/r-sesem-1.0.2-r36h6115d3f_0.tar.bz2  5 years and 25 days ago 128 main

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