Tidybayes supports two classes of models and sample formats: Models/formats that provide prediction functions, and those that do not.
All supported models/formats support the base tidybayes sample extraction functions, such as
tidy_draws(), spread_draws(), gather_draws(), spread_rvars(), and gather_rvars().
These models/formats include:
rstan models
cmdstanr models
brms::brm() models
rstanarm models
runjags::runjags() models
rjags::jags.model() models, if sampled using rjags::coda.samples()
jagsUI::jags() models
MCMCglmm::MCMCglmm() models
coda::mcmc() and coda::mcmc.list() objects, which are output by several model
types.
posterior::draws objects
Any object with an implementation of posterior::as_draws_df() or posterior::as_draws().
For a list of those available in your environment, run methods(as_draws_df) or methods(as_draws)
Any object with an implementation of coda::as.mcmc.list().
For a list of those available in your environment, run methods(as.mcmc.list)
If you install the tidybayes.rethinking package, models from the rethinking package are also supported.
In addition, the following models support fit and prediction extraction functions, such as
add_epred_draws(), add_predicted_draws(), add_linpred_draws(), add_epred_rvars(),
add_predicted_rvars(), and add_linpred_rvars():
brms::brm() models
rstanarm models
any package with implementations of rstantools::posterior_epred(),
rstantools::posterior_predict(), or rstantools::posterior_linpred() that
include an argument called newdata which takes a data frame of predictors.
If your model type is not in the above list, you may still be able to use the add_draws()
function to turn matrices of predictive draws (or fit draws) into tidy data frames. Or,
you can wrap output from a prediction function in posterior::rvar() and add it
to a data frame so long as that output is a matrix with draws as rows.
If you install the tidybayes.rethinking package, models from the rethinking package are also supported.
To include basic support for new models, one need only implement the tidy_draws() generic function
for that model. Alternatively, objects that support posterior::as_draws() or coda::as.mcmc.list()
will automatically be supported by tidy_draws().
To include support for estimation and prediction, one must either implement the epred_draws(),
predicted_draws(), and linpred_draws() functions or their correspond functions from
rstantools: rstantools::posterior_epred(), rstantools::posterior_predict(), and
rstantools::posterior_linpred(). If you take the latter approach, you should include
newdata and ndraws arguments that work as documented in predicted_draws().