A Rank-Based Conjoint (RBC) model consists in exposing respondents to a set of products profiles described in terms of the same attributes (with varying levels) and ask them to rank the product profiles from the best to the worst.
Respondents are not necessarily exposed to the same number of scenarios, however each respondent must provide a rank value for each scenario seen.
The functions RBC.logit.aggregate and RBC.logit.individual available in R-sw Conjoint allow running aggregate-level and individual-level traditional logit estimation of Rank-based conjoint data, respectively. RBC.HBlogit performs an individual-level hierarchical Bayes (HB) logit estimation. Data can easily be prepared for estimation by RBCtext2list.