I'm just collecting some todo items here from existing issues:
Consider adding support for naming the attribute levels in the priors arguments for cbc_choices and cbc_design (see #24).
Consider adding sigma as another argument to cbc_design for Bayesian D-efficient designs to have more control over priors (see here).
Minimize overlap / avoid full overlap in cbc_design #30.
Randomize order across respondents #29
full / orthogonal methods return NA #28
Update cbc_power() to return a new class object of all of the estimated models and then create a print and summary method for this class that shows key information (see here).
I'm just collecting some todo items here from existing issues:
priorsarguments forcbc_choicesandcbc_design(see #24).sigmaas another argument tocbc_designfor Bayesian D-efficient designs to have more control over priors (see here).cbc_design#30.NA#28cbc_power()to return a new class object of all of the estimated models and then create aprintandsummarymethod for this class that shows key information (see here).