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USBen Goodrich
 

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  1. USBen Goodrich
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    1. COI may be able to help, but you would need to post the Stan code (either here or on the stan-users Google group). It is true that Stan does not support discrete parameters, but as far as I know, you can use a continuous latent vector and then the ranks of that would be the posterior order. But if it is really necessary to use discrete parameters it is often possible to marginalize the log-posterior over them. There are several examples in the Stan manual, but the details depend on the particular problem.
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      1. POHow to speed up the rjags model training in Bayesian ranking?
      1. USBen Goodrich
    2. COSince Stan works directly with the log-posterior rather than parsing a graphical model, there shouldn't be a technical problem. So, if you can write down the log-posterior, then it should be fine. Presumably there would be a latent vector of length K, a likelihood of observing the pairwise preferences given the latent vector, and some priors.
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      1. POHow to speed up the rjags model training in Bayesian ranking?
      1. USBen Goodrich
    3. COIf it is feasible to install R >= 3.0.1, then this error can most likely be circumvented by unsetting the environmental variables R_MAKEVARS_SITE and R_MAKEVARS_USER. For example, do `Sys.unsetenv('R_MAKEVARS_SITE')` and `Sys.unsetenv('R_MAKEVARS_USER')`. See the section entitled "Package Installation" in the release notes for R 3.0.1 and `help(Sys.unsetenv)`.
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      1. POError instaling rstan on Rstudio Server
      1. USBen Goodrich
 

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