Number of proposals before any MCMC sampling is done. It typically is set to a fairly large number.

Number of proposals between sampled statistics.

Specifies the proposal (directly) and/or a series of "hints" about the structure of the model being sampled. The specification is in the form of a one-sided formula with hints separated by + operations. If the LHS exists and is a string, the proposal to be used is selected directly.

A common and default "hint" is ~sparse, indicating that the network is sparse and that the sample should put roughly equal weight on selecting a dyad with or without a tie as a candidate for toggling.

Specifies the proposal distribution used in the MCMC Metropolis-Hastings algorithm. Possible choices depending on selected reference and constraints arguments of the ergm() function, but often include "TNT" and "random", and the "default" is to use the one with the highest priority available.

An alternative, direct way of specifying additional arguments to proposal.

if not 0 or NULL, sample about this many networks per call to the lower-level code; this can be useful if output= is a function, where it can be used to limit the number of networks held in memory at any given time.

Set MCMC.effectiveSize to a non-NULL value to adaptively determine the burn-in and the MCMC length needed to get the specified effective size; 50 is a reasonable value. In the adaptive MCMC mode, MCMC is run forward repeatedly (MCMC.samplesize*MCMC.interval steps, up to MCMC.effectiveSize.maxruns times) until the target effective sample size is reached or exceeded. After each run, the returned statistics are mapped to the estimating function scale, then a broken stick model is fit to each statistic to find the candidate burn-in. If its Geweke diagnostic produces a p-value higher than MCMC.effectiveSize.burnin.pval, it is accepted. The effective size of the post-burn-in sample is computed via Vats, Flegal, and Jones (2015), and compared to the target effective size. If it is not matched, the MCMC run is resumed, with the additional draws needed linearly extrapolated but weighted in favor of the baseline MCMC.samplesize by the weighting factor MCMC.effectiveSize.damp (higher = less damping). Lastly, if after an MCMC run, the number of samples equals or exceeds 2*MCMC.samplesize, the chain will be thinned by 2 until it falls below that, while doubling MCMC.interval. MCMC.effectiveSize.order.max can be used to set the order of the AR model used to estimate the effective sample size and the variance for the Geweke diagnostic.

The maximum number of edges that may occur during the MCMC sampling. If this number is exceeded at any time, sampling is stopped immediately.

Names of packages in which to look for change statistic functions in addition to those autodetected. This argument should not be needed outside of very strange setups.

Logical: If TRUE, plot traceplots of the MCMC sample.

R class with which to output networks. The options are "network" (default) and "edgelist.compressed" (which saves space but only supports networks without vertex attributes)

A list of additional arguments to be passed to term initializers. See ? term.options.

Number of threads in which to run the sampling. Defaults to 0 (no parallelism). See the entry on parallel processing for details and troubleshooting.

API to use for parallel processing. Supported values are "MPI" and "PSOCK". Defaults to using the parallel package with PSOCK clusters. See ergm-parallel

Logical: If TRUE, check that the version of ergm running on the slave nodes is the same as that running on the master node.

Logical: If TRUE, slave nodes and processes inherit the set.MT_terms() setting.

A dummy argument to catch deprecated or mistyped control parameters.

For control.simulate.ergm() inheriting MCMC.burnin and MCMC.interval from the ergm fit, the multiplier for the inherited values. This can be useful because MCMC parameters used in the fit are tuned to generate a specific effective sample size for the sufficient statistic in a large MCMC sample, so the inherited values might not generate independent realisations.