THard=-n

You can replicate THard by re-running the fitted model, if I understand the THard argument right (Mike N., correct me if I'm wrong on that). We start cold every time you re-run the model, so it should automatically reset the estimated covariance of the parameters.

A helper function can replicate this effect by just calling mxRun repeatedly on the model. Admittedly, there's some overhead involved in doing so, since the model has to be re-flattened and reprocessed by the back-end. If we want a full implementation of this, it should be done in the back-end for the sake of performance. We'll put the feature on the list and you can use the helper in the meantime.

The helper function would be something like this (sorry it's messy--this is off the top of my head).

tryHard <- function(model, n, ...) {
fit <- mxRun(model)
while(fit@output$status[[1]] == 6 & n > 2) {
print(paste("Trying hard...", n-1, "more times."))
fit <- mxRun(fit)
n <- n - 1
}
fit <- mxRun(fit, ...)
return(fit)
}

The last re-run includes the original arguments, so it will do things like calculate confidence intervals, etc.
There are a few cases where it'll run an extra time to calculate CIs, but if it's already converged, the last run should be quick.