Temporal and cross-sectional network autocorrelation models. These are models for variation in attributes of nodes nested in a network (e.g., drinking behavior of adolescents nested in a school class, or democracy versus autocracy of countries nested in the network of international relations). These models can be estimated for cross-sectional data or panel data, with complex network dependencies as predictors, multiple networks and covariates, arbitrary outcome distributions, and random effects or time trends. Basic references: Doreian, Teuter and Wang (1984)
|Author||Philip Leifeld [aut, cre], Skyler J. Cranmer [ctb]|
|Date of publication||2017-04-01 06:30:55 UTC|
|Maintainer||Philip Leifeld <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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