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allow_negative_values argument to
fit_metad, which models M-ratio on the identity (rather
than the logarithmic) scale to allow negative values of M-ratiolog_M arguments to simulation functions are renamed to
reflect thistype1_probabilities, type2_probabilities,
joint_probabilities, roc1, roc2,
and mean_confidence), allowing for easy comparison with
model estimatesauroc1, auroc2, auroc1_draws, and
auroc2_draws)by_response argument to roc2_draws
and related functions, allowing for type 2 ROCs collapsed across type 1
responses.stimulus, .response,
.confidence, and .joint_response arguments to
all draws functions, allowing users to specify the names of the
corresponding columnsroc2_drawsposterior_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.