Temporal Network Autocorrelation Models (TNAM)

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) <doi:10.1177/0049124184013002001>; Hays, Kachi and Franzese (2010) <doi:10.1016/j.stamet.2009.11.005>; Leenders, Roger Th. A. J. (2002) <doi:10.1016/S0378-8733(01)00049-1>.

Tests Vignettes


Imports/Depends/LinkingTo/Enhances (9)
  • R
  • xergm.common >= 1.7.7
  • network
  • sna
  • igraph
  • vegan
  • lme4 >= 1.0
  • Rcpp >= 0.11.0
  • Rcpp
  • Suggests (1)
  • texreg
  • Version History