Based on use by the SEMCOG team and conversation with @jpn-- , we'll make a few little improvements to the notebook examples:

• add the number of iterations to the estimate call: m.estimate(method='SLSQP', options={'maxiter’:1000})
• speed up writing the Excel report: m.to_xlsx(“filename.xlsx", data_statistics=False)
• set the av (availability) vector based on util size term > 0
• filter out choosers who don't make a valid observed choice
• update the dependencies to use larch.util.activitysim instead of asim_larch.py for better packaging
• add m.parameter_summary() to the notebook reporting so the user can see the t-stats
• add text above the estimate call to explain when to use SLSQP versus BHHH