Imports: statnet.common (>= 4.4.0), coda (>= 0.19.2), RColorBrewer (>=

person("Pavel N.", "Krivitsky", role=c("aut","cre"), email="p.krivitsky@unsw.edu.au", comment=c(ORCID="0000-0002-9101-3362")),
Imports: statnet.common (>= 4.5.0), coda (>= 0.19.2), RColorBrewer (>=
Description: Utilities for managing egocentrically sampled network data and a wrapper around the 'ergm' package to facilitate ERGM inference and simulation from such data. See Krivitsky and Morris (2017) <doi:10.1214/16-AOAS1010>.
Maintainer: Pavel N. Krivitsky <p.krivitsky@unsw.edu.au>

Imports: statnet.common (>= 4.5.0), RColorBrewer (>= 1.1.2), purrr (>=
0.3.2), tibble (>= 2.1.1), dplyr, survey, stats, methods

Title: ergm.graphlets: A Package for ERG Modeling Based on Graphlet
Authors@R: c( person("Omer Nebil", "Yaveroglu", role=c("aut", "cre"), email="o.yaveroglu10@imperial.ac.uk"),
person("Sean M.", "Fitzhugh", role=c("aut"), email="sean.fitzhugh@uci.edu"),
person("Carter T.", "Butts", role=c("aut"), email="buttsc@uci.edu"))
Description: A package integrating graphlet statistics based model terms
for use in "ergm" models as part of the "statnet" suite of packages.
Author: Omer Nebil Yaveroglu [aut, cre],
Maintainer: Omer Nebil Yaveroglu <o.yaveroglu10@imperial.ac.uk>

Title: ERG Modeling Based on Graphlet Properties
Description: Integrates graphlet statistics based model terms for use in exponential-family random graph models ('ergm') as part of the 'statnet' suite of packages.
Packaged: 2015-06-11 09:37:25 UTC; omernebil

Authors@R: c(person("Pavel N.", "Krivitsky", role=c("aut","cre"), email="pavel@statnet.org", comment=c(ORCID="0000-0002-9101-3362")),
Imports: statnet.common (>= 4.6.0), rlang, purrr (>= 0.2.4), tibble (>=
1.4.2), Matrix, methods, parallel, glue, rle, Rdpack (>= 0.8.2)
Suggests: rmarkdown, knitr, dplyr, testthat
Description: A set of extensions for the 'ergm' package to fit multilayer/multiplex/multirelational networks and samples of multiple networks. 'ergm.multi' is a part of the Statnet suite of packages for network analysis. See Krivitsky, Koehly, and Marcum (2020) <doi:10.1007/s11336-020-09720-7> and Krivitsky, Coletti, and Hens (2022) <doi:10.48550/arXiv.2202.03685>.