The results from 2 programs are conflicting on convergence issues in my multivariate logistic regression, how do I deal with this? Currently I am analyzing a dataset using logistic regression,  I ran it in R using the glm function to run a multivariate logistic regression with 12 predictors. Some of these are quite collinear as they resulted from feature engineering. I was thus not really surprised that my algorithm did not converge and turned out NA for some of the engineered features. I proceeded to run it in two parts and report the results in that way.
However, my collaborator wanted to check the results to make sure no error occurred. She prefers using JMP and thus ran it in that software with the built-in analysis available. JMP reported estimates for all parameters without complaining about convergence.
My question is thus, how do I deal with this? Are the parameters estimates obtained sound or might this indicate hidden convergence issues?

Software implementations of the same model often use different estimation methods, so the fact glm and JMP (which I am not familiar with) produce different results is no surprise. Further, even if they both used the same estimation algorithm they may do so with different default settings (e.g., convergence criteria, starting values), which may also lead to different results. So the fact that one implementation of your logistic regression model did not converge is not surprising.
If I were in your position, I would compare how glm and JMP estimate your model parameters. However, if you are not interested, I would suggest simply using results obtained from JMP (i.e., the software where your model converged) since convergence issues should never be ignored.
Finally, you could try using point estimates obtained from JMP as starting values in glm. It would be interesting to see if this would help with convergence.
