Changes in version 1.3.2.9000                      

                 Changes in version 1.3.2 (2025-07-23)                  

Fix the compatibility of plotting tests with upcoming ggplot2 version.

                 Changes in version 1.3.1 (2023-09-11)                  

  - Fix the description of the number of raters in the anesthesia data.
    The documentation had erroneously stated there were four
    anesthetists, not five.

  - Update the stan code for compatibility with rstan v2.26.0
    (@andrjohns)

                 Changes in version 1.3.0 (2023-03-11)                  

  - Updated the Stan implementation, priors, and initialisation points
    of the hierarchical Dawid-Skene model, leading to much more reliable
    convergence.

  - Added the ability to visualise the theta parameter with uncertainty.

  - Added row names to the output of class_probabilities().

  - Added the ability to specify the column names of long format data
    passed to rater().

  - Added simulate_dawid_skene_model() and
    simulate_hier_dawid_skene_model() to simulate data from the
    Dawid-Skene and hierarchical Dawid-Skene models.

  - Re-export loo_compare().

  - Allowed the theta parameter to be extracted from the hierarchical
    Dawid-Skene model.

                 Changes in version 1.2.0 (2021-07-13)                  

  - Add waic() function for model comparison

  - Silence warnings with the latest ggplot2 version

  - Fix validation bug in posterior_predict()

                 Changes in version 1.1.0 (2021-03-14)                  

  - 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.

                 Changes in version 1.0.0 (2020-09-15)                  

  - Initial release