Replaced the former multi-step workflow with fit(),
which accepts an empirical data frame or simulates data when
data = NULL.
Added correlated and uncorrelated normal, positive log-normal,
and negative log-normal random effects through named
random_effects specifications.
Added latent_class_effects to fit(),
which names the effects that differ between the latent classes.
Added interpret(), which reads the estimates as
compensations between effects, such as the willingness to pay, and as
average marginal effects or marginal effects at the average covariate
values, each with posterior uncertainty.
Added the update() method for fitted models, which
refits with a modified specification.
Added decider-level WAIC and PSIS-LOO and replaced the harmonic-mean marginal-likelihood estimator with bridge-sampling Bayes factors.
Reworked predict() and residuals()
around posterior uncertainty.
Added posterior diagnostics and standard bayesplot
displays through summary() and
plot().
Improved numerical efficiency of the Gibbs sampler.
Rewrote the vignettes: getting started, model variants, preference heterogeneity, posterior prediction, and model evaluation.
Fix in npar.RprobitB_fit(). Parameters for the
covariance matrices of the mixing distributions have been
miscounted.
Fix in sufficient_statistics(). There was a bug in
calculating sufficient statistics in the ordered case.
Added function mode_approx() to approximate
conditional posterior modes.
Documentation improvements.
Small fixes.
Added train_choice data set.
logLik() method now returns a logLik
object that can be passed to AIC() and BIC()
methods from {stats}.
Small bug fixes for the ordered probit model case.
Ranked probit models
Ordered probit models
Print method for RprobitB_parameter.
Splitting the data set into a train and test part can now be done
by the function train_test(). Consequently, the argument
test_prob was removed in prepare_data() and
simulate_choices().
The function simulate_choices() does not have the
argument distr anymore. Instead, covariates can be supplied
via the covariates argument. Consequently, the argument
standardize was removed as well.
Simplified specifying the utility scale, see the
help page of RprobitB_normalization().
The function compare is now called
model_selection().
The function prepare is now called
prepare_data().
The function simulate is now called
simulate_choices() to not mask
stats::simulate().
The function mcmc is now called
fit_model().
The function choice_probs is now called
choice_probabilities().
Integrated S3 classes and methods.
Several new functionalities.