I noticed that when I use like.func='t' and add "chisq" to Data$mon.names they get swapped (i.e. labelled wrongly) in the Summary1 output of LaplacesDemon. So for my example galaxy, profitLikeModel gives out a chisq of ~4e6 and dof ~ 2; while in the "Mean" column of LDfit$Summary1 I have a chisq ~2 and dof ~4e6. Not sure where exactly it gets muddled up, but I guess one of the functions is assuming mon.names to be in a certain order when they're not (either profitSetupData, profitLikeModel or LaplacesDemon).
I was also wondering what the "dof" calculated by profitLikeModel mean? I can see how it's calculated in the code but I don't understand the physical/statistical interpretation (I have 8609 pixels and 7 fitting parameters, so the degrees of freedom of the fit should be 8602, i.e. it can't be that). I'm asking for general understanding and because it seems to be the best metric I came across so far to judge whether or not a galaxy is well-fitted by a single Sersic profile using the single Sersic fits alone (which I just noted by chance when seeing that the distribution of "dof" is highly bimodal... I wasn't even particularly looking for such a metric, but it might come in handy).
That looks an order bug when monitoring the Student-T (I didn't write that bit in any case). I *think* I've just pushed a fix.
For the other thing for dof, this is a slightly different meaning of dof specific to the Student-T distribution. Check the Wiki page: en.wikipedia.org/wiki/Student%27s_t-distribution. What we are doing is maximising the likelihood for a given distribution (like measuring the SD for a Normal). If that seems to be useful to you then great (but not obvious to me why yet...)