Title: Local Health Department network data set
Maintainer: Jenine K. Harris <harrisj@wustl.edu>
Description: Data for use with the Sage Introduction to Exponential
Random Graph Modeling text by Jenine K. Harris.  Network data
set consists of 1283 local health departments and the
communication links among them along with several attributes.

Title: Exponential Random Graph Models for Small Networks
Description: Simulation and estimation of Exponential Random Graph Models (ERGMs)
for small networks using exact statistics. As a difference from the 'ergm'
package, 'ergmito' circumvents using Markov-Chain Maximum Likelihood Estimator
(MC-MLE) and instead uses Maximum Likelihood Estimator (MLE) to fit ERGMs
for small networks. As exhaustive enumeration is computationally feasible for
small networks, this R package takes advantage of this and provides tools for
calculating likelihood functions, and other relevant functions, directly,
meaning that in many cases both estimation and simulation of ERGMs for
small networks can be faster and more accurate than simulation-based
person(given = "George", family = "Vega Yon", role = c("cre","aut"),
email = "g.vegayon@gmail.com", comment = c(ORCID = "0000-0002-3171-0844")),
person(given = "Kayla", family = "de la Haye", role = c("ths"),
person("Army Research Laboratory and the U.S. Army Research Office",
role = "fnd", comment = "Grant Number W911NF-15-1-0577")
Imports: ergm, network, MASS, Rcpp, texreg, stats, parallel, utils,
Suggests: covr, sna, lmtest, fmcmc, coda, knitr, rmarkdown, tinytest
Collate: 'RcppExports.R' 'boot.R' 'count_stats.R' 'data.R'
'random.R' 'same_dist.R' 'sim.R' 'simulate.R' 'texreg.R'
Author: George Vega Yon [cre, aut] (<https://orcid.org/0000-0002-3171-0844>),
Army Research Laboratory and the U.S. Army Research Office [fnd] (Grant

for small networks using exact statistics as shown in Vega Yon et al. (2020)
<DOI:10.1016/j.socnet.2020.07.005>. As a difference from the 'ergm'

Title: Equi-Rank Hierarchical Clustering Validation
Description: Assesses the statistical significance of clusters for a given dataset through bootstrapping and hypothesis testing of a given matrix of empirical Spearman's rho, based on the technique of S. Gaiser et al. (2010) <doi:10.1016/j.jmva.2010.07.008>.
Imports: igraph, stringr, stringi, utils, Rdpack
Packaged: 2018-06-08 11:06:14 UTC; Simon-Pierre