Add

`waic()`

function for model comparisonSilence warnings with the latest ggplot2 version

Fix validation bug in

`posterior_predict()`

`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 ratersâ€™ 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 ratersâ€™ 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.

- Initial release