NEWS.md
__default_factor and __default_continuous priors in JAGS_formula() - when specified in the prior_list, these are used as default priors for factor and continuous predictors that are not explicitly specifiedformula_scale parameter in JAGS_formula() and JAGS_fit() - improves MCMC sampling efficiency and numerical stabilitytransform_scale_samples() function to transform posterior samples back to original scale after standardizationtransform_prior_samples() function to generate and transform prior samples using the same matrix transformation as posterior samples - enables correct visualization of priors on the original (unscaled) predictor scale, including proper handling of the intercept which depends on multiple coefficient priorstransform_scaled argument to plot_posterior() for visualizing prior and posterior distributions on the original (unscaled) scale when using formula-based models with auto-scalingexp_lin transformation type for log-intercept unscaling in density/plotting functions: exp(a + b * log(x))
log(intercept) formula attribute for specifying models of the form log(intercept) + sum(beta_i * x_i) - useful for parameters that must be positive (e.g., standard deviation) while keeping the intercept on the original scale. Set via attr(formula, "log(intercept)") <- TRUE. Supported in JAGS_formula(), JAGS_evaluate_formula(), and marginal likelihood computationrunjags_estimates_table():
remove_parameters = TRUE to remove all non-formula parametersremove_formulas to remove all parameters from specific formulaskeep_parameters to keep only specified parameterskeep_formulas to keep only parameters from specified formulasbias is specified in remove_parameters or keep_parameters, the corresponding bias-related parameters (PET, PEESE, omega, alpha, pi_null, and phack_kind) are automatically included based on the bias prior typeprobs argument to runjags_estimates_table() and runjags_estimates_empty_table() for custom quantiles (default: c(0.025, 0.5, 0.975))effect_direction argument to plot_posterior(), plot_prior_list(), lines_prior_list(), and geom_prior_list() for PET-PEESE regression plots - use "positive" (default) for mu + PET*se + PEESE*se^2 or "negative" for mu - PET*se - PEESE*se^2
prior_weightfunction() around a unified side, steps, and weights specification, with wf_cumulative(), wf_fixed(), and wf_independent() constructors for cumulative Dirichlet, fixed, independent, and log-independent weightfunction priorsprior_phacking(), prior_bias(), calibration helpers, and selection_backend_spec() for compiling active step/p-hacking backend parametersrunjags_estimates_table() and stan_estimates_table() from lCI/Median/uCI to numeric values (e.g., 0.025/0.5/0.975) for consistency with ensemble summary tablesomega representationNA when the prior assigns probability 0 or 1 to inclusion, while keeping finite-sample bounds for posterior inclusion probabilities of 0 or 10 (instead of only -1).is.wholenumber with NAs and na.rm = TRUE
plot_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) notationmax_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 = TRUE
student-t allowed as a prior distribution name
JAGS_evaluate_formula
runjags_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 distributionsplot_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 functioninclusion_BF to deal with over/underflow (Issue #9)ensemble_inference_table() (Issue #11)ensemble_summary_table (Issue #7)plot_posterior fails with only mu & PET samples (Issue #5)