JAGS_diagnostics functions now correctly handle factor
parameters nested within mixture priorsplot_posterior() function with spike and slab
priorsprior_mixture() and
prior_spike_and_slab()JAGS_formula() function now replaces removed missing
intercept with 0 (so the model matrix remains unchanged)silent = FALSE argument in the
JAGS_fit() function now fits the model non-silently
againexpression()
instead of a parameter, such objects can be use to create prior
distributions that depend on other parameters in JAGSJAGS_fit() function
to accept expressions that are appended as literal text to the generated
JAGS formulaJAGS_fit() function
to handle uncorrelated random effects via (x||y)
(lme4-like) notationJAGS_estimates_table not printing formula prefix when
only spike and slab priors are suppliedmax_extend option to autofit_control
argument in JAGS_fit() to limit the number of iterations
for the model extensionJAGS_diagnostics_density() plots for mixture
distributionsplot_posterior() for simple
as_mixed_posteriors objectsJAGS_evaluate_formula() for mixture and spike and slab
priors.fit_to_posterior()prior_mixture() function for creating a mixture
of prior distributionsas_mixed_posteriors() and
as_marginal_inference() functions for a single JAGS models
(with spike and slab or mixture priors) to enabling tables and figures
based on the corresponding outputinterpret2() function for another way of
creating textual summaries without the need of inference and samples
objectsrunjags_estimates_table() functionprior_informed() functionbridge_object() (fixes:
https://github.com/FBartos/BayesTools/issues/28)Na/NaN tests for check_ functions
(fixes: https://github.com/FBartos/BayesTools/issues/26)JAGS_extend()
functionautofit_control argument in
JAGS_fit(): "restarts" allows to restart model
initialization up to restarts times in case of failuremodel_summary_table() in case of
prior_none()contrast = "meandif" to the
prior_factor function which generates identical prior
distributions for difference between the grand mean and each factor
levelcontrast = "independent" to the
prior_factor function which generates independent identical
prior distributions for each factor levelremove_column function for removing columns from
BayesTools_table objects without breaking the attributes
etc…remove_parameters argument to
model_summary_table()point prior distribution as option to
prior_factor with "meandif" and
"orthonormal" contrastsmarginal_posterior() function which creates
marginal prior and posterior distributions (according to a model formula
specification)Savage_Dickey_BF() function to compute density
ratio Bayes factors based on marginal_posterior
objectsmarginal_inference() function to combine
information from marginal_posterior() and
Savage_Dickey_BF()marginal_estimates_table() function to summarize
marginal_inference() objectsplot_marginal() function to visualize
marginal_inference() objectscontrast = "meandif" is now the default setting for
prior_factor functiontransform_orthonormal argument in favor of
more general transform_factors argumentdummy contrast/factor attributes to
treatment for consistency
(https://github.com/FBartos/BayesTools/issues/23)check_bool(),
check_char(), check_real(),
check_int(), and check_list() do not throw
error if allow_NULL = TRUEstudent-t allowed as a prior distribution
nameJAGS_evaluate_formularunjags_estimates_table() function can now handle
factor transformationsplot_posterior function can now handle factor
transformationsrunjags_estimates_table() function via the
remove_parameters argumentrunjags_estimates_table() function can now remove
factor spike prior distributionsstan_estimates_summary() functionJAGS_marglik_parameters_formula functionrunjags_estimates_table functionensemble_summary_table and
ensemble_diagnostics_table function can create table
without model componentsJAGS_evaluate_formula for evaluating formulas based on
data and posterior samples (for creating predictions etc)JAGS_parameter_names for transforming formula names
into the JAGS syntaxplot_models implementation for factor predictorsformat_parameter_names for cleaning parameter names
from JAGSmean, sd, and var functions
now return the corresponding values for differences from the mean for
the orthonormal prior distributionsrunjags_summary_table function
(previous version crashed under other than default fit_JAGS
settings)runjags_summary_table functionplot_models functionadd_column function for extending
BayesTools_table objects without breaking the attributes
etc…BayesTools_table functions with with
formula_prefix argumentmultiply_by attribute passed with the
prior)plot_posterior (posterior is now plotted over the
prior)inclusion_BF to deal with
over/underflow (Issue #9)ensemble_inference_table() (Issue #11)ensemble_summary_table
(Issue #7)prior_informed function for creating informed prior
distributions based on the past psychological and medical researchprior.plot can’t plot “spike” with
plot_type == "ggplot" (Issue #6)MCMC error/SD print names in BayesTools tables (Issue
#8)JAGS_bridgesampling_posterior unable to add a parameter
via add_parametersinterpret function for creating textual summaries based
on inference and samples objectsplot_posterior fails with only mu & PET samples
(Issue #5)
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