tnam: 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>.

Version: 1.6.5
Depends: R (≥ 2.14.0), xergm.common (≥ 1.7.7)
Imports: methods, utils, stats, network, sna, igraph, vegan, lme4 (≥ 1.0), Rcpp (≥ 0.11.0)
LinkingTo: Rcpp
Suggests: texreg
Published: 2017-04-01
Author: Philip Leifeld [aut, cre], Skyler J. Cranmer [ctb]
Maintainer: Philip Leifeld <philip.leifeld at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: tnam citation info
CRAN checks: tnam results


Reference manual: tnam.pdf
Package source: tnam_1.6.5.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: tnam_1.6.5.tgz, r-oldrel: tnam_1.6.5.tgz
Old sources: tnam archive

Reverse dependencies:

Reverse depends: xergm


Please use the canonical form to link to this page.