tbergm: Estimate a TERGM using Bayesian estimation

In btergm: Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

Estimate a TERGM using Bayesian estimation

Description

Estimate a TERGM using Bayesian estimation.

Usage

tbergm(formula, returndata = FALSE, verbose = TRUE, ...)

Arguments

formula	Formula for the TERGM. Model construction works like in the ergm package with the same model terms etc. (for a list of terms, see help("ergm-terms")). The networks to be modeled on the left-hand side of the equation must be given either as a list of network objects with more recent networks last (i.e., chronological order) or as a list of matrices with more recent matrices at the end. dyadcov and edgecov terms accept time-independent covariates (as network or matrix objects) or time-varying covariates (as a list of networks or matrices with the same length as the list of networks to be modeled).
returndata	Return the processed input data instead of estimating and returning the model? In the btergm case, this will return a data frame with the dyads of the dependent variable/network and the change statistics for all covariates. In the mtergm case, this will return a list object with the blockdiagonal network object for the dependent variable and blockdiagonal matrices for all dyadic covariates and the offset matrix for the structural zeros.
verbose	Print details about data preprocessing and estimation settings.
...	Further arguments to be handed over to the bergm function in the Bergm package.

Details

The tbergm function computes TERGMs by Bayesian estimation via blockdiagonal matrices and structural zeros. It acts as a wrapper for the bergm function in the Bergm package.

Author(s)

Philip Leifeld

References

Caimo, Alberto and Nial Friel (2012): Bergm: Bayesian Exponential Random Graphs in R. Journal of Statistical Software 61(2): 1-25. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v061.i02")}.

See Also

btergm mtergm

btergm documentation built on April 3, 2025, 9:01 p.m.