rbernoulli

Simulation and estimation of Exponential Random Graph Models (ERGMs)
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'
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
algorithms.

George Vega Yon

ergmito

Exponential Random Graph Models for Small Networks

Kayla de la Haye

Army Research Laboratory and the U.S. Army Research Office 

rbernoulli function

<dl><dt>n</dt>
<dd>Integer vector. Size of the graph. If <code>length(n) &gt; 1</code>, then it will
a list of random graphs.</dd>
<dt>p</dt>
<dd>Probability of a tie. This may be either a scalar, or a vector of the
same length of <code>n</code>.</dd></dl>

Arguments

Random Bernoulli graph — rbernoulli

<dl>

<dt>n</dt>
<dd>Integer vector. Size of the graph. If <code>length(n) &gt; 1</code>, then it will
a list of random graphs.</dd>


<dt>p</dt>
<dd>Probability of a tie. This may be either a scalar, or a vector of the
same length of <code>n</code>.</dd>

</dl>

rbernoulli: Random Bernoulli graph

Description

Usage

Value

Arguments

Examples