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posterior_predict() and
posterior_epred() for models with multilevel effects on a
single confidence levelcov_matrix() now works for scalar inputsaggregate_metad() now removes rows with NA
values prior to aggregationmetac2_parameters() function streamlines setting priors
for confidence criteriaaggregate_metad() and fit_metad() now
perform more thorough checks on the number of confidence levels,
Kaggregate_metad() has increased efficiencylinpred_draws_metad/linpred_rvars_metad where
meta_c only used first drawlogit option to use Stan’s
multinomial_logit_lpmf/categorical_logit_lpmfaggregate_metad() now preserves column
types
aggregate_metad() and fit_metad() now
infer K using the maximum confidence level (instead of the
number of unique levels)
aggregate_metad() and fit_metad() now
have more helpful errors/messages for invalid data arguments
Minor updates to package documentation
hmetad is now on CRAN!
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.