text: Tools for simulating POMDP-AIF models were originally developed as part of the 
  DEM [20] library for MATLAB [21] (part of the larger SPM library [22]). Since then, a 
  modal and flexible software package pymdp [23] was created for Python [24], as well as a 
  performance-oriented package cpp-AIF [25] for C++ [26] that can be used across platforms. 
  Finally, the factor graph library RxInfer [27] for Julia [28] has also been used to implement 
  some AIF models on an efficient factor graph back-end [29–31]. The important tools 
  that these packages provide make AIF available for researchers to perform simulation 
  studies and for use in engineering contexts. They do not, however, usually allow for 
  fitting models to empirically observed data, which is a fundamental method used in 
  cognitive modelling [32], often in the context of computational psychiatry [13], to infer the 
  mechanisms underlying variations in behaviour or to investigate the differences between 
  (for example, clinical) populations. Smith and colleagues [33] provided a guide for manually 
  doing variational Bayesian parameter estimation based on empirical data, but only in 
  MATLAB and restricted to a particular class of variational parameter estimation methods 
  (variational Laplace), instead of the sampling-based methods that currently predominate in 
  the field of cognitive modelling [34,35]. 