rater 1.1.0

• summary() now works with the class conditional and hierarchical Dawid-Skene models.

• All functions applied to fitted class conditional Dawid-Skene models will automatically convert the relevant parameters of the model into a full theta parameter equivalent to the Dawid-Skene model. This is designed to allow easier comparison of the class conditional model with the full Dawid-Skene model.

• Plotting via plot() of the rater_fit object has been changed in several ways. plot.rater_fit now:

  • Only returns one plot
  • Only returns the theta plot by default
  • Exposes the prob, which (called rater_index) and new item_index
    arguments in the plot generic.

• Add the ability to only plot a subset of items when plotting the class probabilities. This can be controlled by the new item_index argument to plot()

• Added the function wide_to_long() to convert wide data to long data.

• Add the option data_format = "wide" to rater() to allow wide data to be passed into rater() directly.

• Added the get_stanfit() function to extract the underlying stanfit object from a rater fit object.

• Added an implementation of the posterior_predict generic from {rstantools} allowing simulation from the posterior predictive distribution of fitted standard, and class conditional, Dawid-Skene models. (The hierarchical Dawid-Skene model is not yet supported).

• Added an implementation of the prior_summary generic from {rstantools} for rater_fit objects.

• Add the loo.rater_fit method to allow the calculation of loo, a modern Bayesian model comparison metric, for rater models. loo values can be compared using the excellent {loo} package.

• Added the loo.rater_fit method to allow the calculation of loo, a modern Bayesian model comparison metric, for rater models. loo values can be compared using the excellent {loo} package.

• Rater specific prior parameters can now be used in the Dawid-Skene model for both grouped and long data. In practice this means that it is now possible to pass a J * K * K array for beta into dawid_skene() which encodes a K * K prior parameter for each of the J raters' error matrices. For backwards compatibility and ease of use it is still possible to pass a single matrix for beta which will still be interpreted as the prior parameter for all the of the raters' error matrices.

• The plot produced for the pi parameter has been changed. The new plot represents the uncertainty in the point estimates when MCMC has been used to fit the model.

• Prior parameters for the Dawid-Skene and class conditional Dawid-Skene models have been altered slightly to improve convergence of optimization when the number of classes is small.

• summary.mcmc_fit now displays the number of remaining parameters correctly.

• Added the as_mcmc.list() function to convert MCMC fits to {coda} mcmc.list objects.