Performs edgewise predictions from a GERGM model fit.

1 2 3 4 5 6 | ```
conditional_edge_prediction(GERGM_Object, simulation_method = c("Metropolis",
"Gibbs"), number_of_networks_to_simulate = 500, thin = 1,
proposal_variance = 0.1, MCMC_burnin = 100, seed = 123,
return_constrained_networks = FALSE, optimize_proposal_variance = FALSE,
target_accept_rate = 0.25, use_stochastic_MH = FALSE,
stochastic_MH_proportion = 1)
``` |

`GERGM_Object` |
A GERGM object output by the gergm() estimation function. The following terms must still be specified: number_of_networks_to_simulate, thin, and MCMC_burnin. proposal_variance may also be specified, or if set equal to NULL, then the proposal variance from parameter estimation will be instead (this option is likely preferred in most situations). |

`simulation_method` |
Default is "Metropolis" which allows for exponential down weighting, can also be "Gibbs". |

`number_of_networks_to_simulate` |
Number of simulations generated for estimation via MCMC. Default is 500. |

`thin` |
The proportion of samples that are kept from each simulation. For example, thin = 1/200 will keep every 200th network in the overall simulated sample. Default is 1. |

`proposal_variance` |
The variance specified for the Metropolis Hastings simulation method. This parameter is inversely proportional to the average acceptance rate of the M-H sampler and should be adjusted so that the average acceptance rate is approximately 0.25. Default is 0.1. |

`MCMC_burnin` |
Number of samples from the MCMC simulation procedure that will be discarded before drawing the samples used for estimation. Default is 100. |

`seed` |
Seed used for reproducibility. Default is 123. |

`return_constrained_networks` |
Logical argument indicating whether simulated networks should be transformed back to observed scale or whether constrained [0,1] networks should be returned. Defaults to FALSE, in which case networks are returned on observed scale. |

`optimize_proposal_variance` |
Logical indicating whether proposal variance should be optimized if using Metropolis Hastings for simulation. Defaults to FALSE. |

`target_accept_rate` |
Defaults to 0.25, can be used to optimize Metropolis Hastings simulations. |

`use_stochastic_MH` |
A logical indicating whether a stochastic approximation to the h statistics should be used under Metropolis Hastings in-between thinned samples. This may dramatically speed up estimation. Defaults to FALSE. HIGHLY EXPERIMENTAL! |

`stochastic_MH_proportion` |
Percentage of dyads/triads to use for approximation, defaults to 0.25 |

A list object containing simulated networks.

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