Dear ALPS community. I am studying Fermionic Hubbard model. And right now I am using QUEST package from quest-qmc.googlecode.com, Algorithm that is used in it is Determinant Quantum Monte Carlo. I do not like how it scales with lattice size and beta (inverse temperature) and I do not like that you should do extra extrapolation to take of the Trotter errors. I was thinking if some other algorithms do not have these problems. I am trying to use ALPS to do study the same model. [1] I've tried and successfully used Full and Sparse diagonalisation routines and they are great( Small wishes: [1][a] It will be nice if diagonalization code could measure 4 operator variables. [1][b] It will be nice if routines were able to use not only translational symmetries, but also point groups. ) [2] I'v tried to use 'dirloop_sse', but I get "Cannot convert fermionic operator to a bosonic matrix" [3] All tutorials about Monte Carlo, that I found in the ALPS package work either with spin or boson models. So, question is: Is there any Monte Carlo routine that works with Fermionic Hubbard model? Questions not related to Physics: [4] Is there any url, similar to http://docs.python.org, where I can find documentation about pyalps library? Right now I am using help(function_name), but this looks like a bad way to get the information. [5] Is there any bug tracker? Thank you for any help.