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>.

Install the latest version of this package by entering the following in R:
install.packages("tnam")
AuthorPhilip Leifeld [aut, cre], Skyler J. Cranmer [ctb]
Date of publication2017-04-01 06:30:55 UTC
MaintainerPhilip Leifeld <philip.leifeld@glasgow.ac.uk>
LicenseGPL (>= 2)
Version1.6.5
http://github.com/leifeld/tnam

View on CRAN

Files

inst
inst/CITATION
src
src/tnam.cpp
src/RcppExports.cpp
NAMESPACE
R
R/tnam-terms.R R/RcppExports.R R/checkDataTypes.R R/tnam.R
MD5
DESCRIPTION
man
man/tnam-package.Rd man/tnam.Rd man/tnam-terms.Rd

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.