The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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)
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.
Add waic() function for model comparison
Silence 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:
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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.