| ergm.count-package {ergm.count} | R Documentation |
Fit, Simulate and Diagnose Exponential-Family Models for Networks with Count Edges
Description
ergm.count is a set of extensions to
package ergm to fit and simulate from
exponential-family random graph models for networks whose edge weights are
counts (Krivitsky 2012).
Details
Mainly, it implements Poisson, binomial, geometric, and discrete
uniform dyadwise reference measures for valued ERGMs (documented
here in ergmReference), and provides some count-specific change
statistics (documented in ergmTerm)
(Krivitsky 2012; Krivitsky et al. 2023), including
CMP for the Conway–Maxwell–Poisson Distribution
(Shmueli et al. 2005).
For a complete list of the functions, use library(help="ergm") and
library(help="ergm.count") or read the rest of the manual.
When publishing results obtained using this package, please cite the
original authors as described in citation(package="ergm.count").
All programs derived from this package must cite it.
This package contains functions specific to using ergm to
model networks whose dyad values are counts. Examples include counts of
conversations, messages, and other interactions.
For detailed information on how to download and install the software, go to the Statnet project website: https://statnet.org. A tutorial, support newsgroup, references and links to further resources are provided there.
Known issues
Parameter space constraints
Poisson- and geometric-reference ERGMs have an unbouded sample space. This
means that the parameter space may be constrained in complex ways that
depend on the terms used in the model. At this time ergm has
no way to detect when a parameter configuration had strayed outside of the
parameter space, but it may be noticeable on a runtime trace plot (activated
via MCMC.runtime.traceplot control parameter), when the simulated
values keep climbing upwards. (See Krivitsky (2012) for a further
discussion.)
A possible remedy if this appears to occur is to try lowering the
control.ergm() parameter MCMLE.steplength.
Author(s)
Maintainer: Pavel N. Krivitsky pavel@statnet.org (ORCID)
Other contributors:
Mark S. Handcock handcock@stat.ucla.edu [contributor]
David R. Hunter dhunter@stat.psu.edu [contributor]
Joyce Cheng joyce.cheng@student.unsw.edu.au [contributor]
References
Krivitsky PN (2012).
“Exponential-family Random Graph Models for Valued Networks.”
Electronic Journal of Statistics, 6, 1100–1128.
doi:10.1214/12-EJS696.
Krivitsky PN, Hunter DR, Morris M, Klumb C (2023).
“ergm 4: New Features for Analyzing Exponential-Family Random Graph Models.”
Journal of Statistical Software, 105(6), 1–44.
doi:10.18637/jss.v105.i06.
Shmueli G, Minka TP, Kadane JB, Borle S, Boatwright P (2005).
“A Useful Distribution for Fitting Discrete Data: Revival of the Conway–Maxwell–Poisson Distribution.”
Journal of the Royal Statistical Society: Series C, 54(1), 127–142.
ISSN 1467-9876, doi:10.1111/j.1467-9876.2005.00474.x.