text: A library of pre-made canonical POMDP models could be created so that users can eas- 
  ily implement them directly. Alternatives to the fixed-point iteration method for updating 
  posteriors over environmental states could be included, like the marginal message passing 
  algorithm. There are various ways in which the package can be made more computationally 
  efficient, and it could be compared with other software implementations. There are plenty 
  of utility and plotting functions that could be added to the package to make it easier to 
  use and to facilitate integration with the model-fitting packages it relies on; for example, 
  to allow for combining the models with linear regressions to compare parameters values 
  of different populations in a single model. More complex types of POMDP models can 
  also be added, like hierarchical and temporally deep POMDPs. Model structure learning 
  could be considered, where different model structures are compared and chosen between 
  by evaluating their free energies. Sophisticated inference, where predictions are also made 
  about changes in one’s own beliefs—depending on expected action-dependent observations 
  in the future—could also be implemented [58]. Finally, the package could be extended to 
  other types of generative models than POMDPs, including other universal models, like 
  generalised filtering [17] and Hierarchical Gaussian Filter models [41], as well as custom 