This version (v1.2.0) revised how MUFASA calculates the AICc relative likelihood to that described by Burnham & Anderson (2004) for least squares estimation with normally distributed errors.
MUFASA's earlier implementation has been shown to be robust through rigorous tests against synthetic spectra (see Chen, M. C-Y. et al. 2020). The improvements brought forward by this version tend to be found in marginal cases where two models provide comparable fits to the data.