PCS_convergence_McCandR: Check a PCS network for convergence
In PCSinR: Parallel Constraint Satisfaction Networks in R

This function applies the convergence criterion defined by McClelland and Rumelhart to a given network, and returns either a (qualitative) boolean value that represents the convergence state, or a (quantitative) value that represents the number of iterations (of the last 10) that have met the convergence threshold.

1 2	PCS_convergence_McCandR(iteration, current_energy, memory.matrix, stability_criterion = 10^-6, output = "qualitative")

`iteration`	The iteration to consider – in most cases, this will be the current iteration during a simulation run, however, the check can also be applied to a model output retroactively, and the iteration specified manually.
`current_energy`	The current energy level within the network
`memory.matrix`	A matrix of iteration, energy and node states (in columns, in that order), across all previous iterations (in rows).
`stability_criterion`	Criterion for stability. Changes below this value are no longer considered significant, and ten iterations without significant changes to the energy level in succession will trigger the convergence check.
`output`	Either `'qualitative'` (default), in which case the check returns a boolean value representing whether it has passed or not, or `'quantitative'`, in which case the number of checked trials for which the convergence criterion was met is returned. This last option is of most value for debugging convergence.