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 glasgow.ac.uk> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
http://github.com/leifeld/tnam |
| NeedsCompilation: |
yes |
| Citation: |
tnam citation info |
| CRAN checks: |
tnam results |
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