btergm: Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs.

Version: 1.6
Depends: R (≥ 2.14.0), xergm.common (≥ 1.6)
Imports: stats4, utils, methods, graphics, statnet (≥ 2015.11.0), statnet.common (≥ 3.3.0), network (≥ 1.13.0), sna (≥ 2.3.2), ergm (≥ 3.5.1), texreg (≥ 1.34), parallel, Matrix (≥ 1.2.2), boot (≥ 1.3.17), coda (≥ 0.18.1), stats, ROCR (≥ 1.0.7), speedglm (≥ 0.3.1), igraph (≥ 0.7.1)
Suggests: RSiena (≥ 1.0.12.169), xergm
Published: 2015-12-14
Author: Philip Leifeld [aut, cre], Skyler J. Cranmer [ctb], Bruce A. Desmarais [ctb]
Maintainer: Philip Leifeld <philip.leifeld at eawag.ch>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: btergm citation info
CRAN checks: btergm results

Downloads:

Reference manual: btergm.pdf
Package source: btergm_1.6.tar.gz
Windows binaries: r-devel: btergm_1.6.zip, r-release: btergm_1.6.zip, r-oldrel: btergm_1.6.zip
OS X Snow Leopard binaries: r-release: btergm_1.6.tgz, r-oldrel: not available
OS X Mavericks binaries: r-release: btergm_1.6.tgz
Old sources: btergm archive

Reverse dependencies:

Reverse depends: xergm
Reverse suggests: broom