Changes in version 0.3.0 (2026-05-06) Features - major refactoring and speed-up of unit tests - adds support for __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 specified - adds automatic standardization of continuous predictors via formula_scale parameter in JAGS_formula() and JAGS_fit() - improves MCMC sampling efficiency and numerical stability - adds transform_scale_samples() function to transform posterior samples back to original scale after standardization - adds transform_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 priors - adds transform_scaled argument to plot_posterior() for visualizing prior and posterior distributions on the original (unscaled) scale when using formula-based models with auto-scaling - adds exp_lin transformation type for log-intercept unscaling in density/plotting functions: exp(a + b * log(x)) - adds 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 computation - adds advanced parameter filtering options to runjags_estimates_table(): - remove_parameters = TRUE to remove all non-formula parameters - remove_formulas to remove all parameters from specific formulas - keep_parameters to keep only specified parameters - keep_formulas to keep only parameters from specified formulas - when bias 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 type - adds probs argument to runjags_estimates_table() and runjags_estimates_empty_table() for custom quantiles (default: c(0.025, 0.5, 0.975)) - adds 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 - redesigns 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 priors - adds p-hacking and composed selection-bias priors via prior_phacking(), prior_bias(), calibration helpers, and selection_backend_spec() for compiling active step/p-hacking backend parameters - adds error % for inclusion BF calculation Changes - changes quantile column names in runjags_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 tables - implied prior distributions for estimated marginal means, unstandardized coefficients, and PET-PEESE no longer require prior samples - implied prior distributions for weightfunction weights now use analytical forms for cumulative Dirichlet, fixed, independent, and log-independent priors, including mixture and model-averaged weightfunctions where possible - independent weightfunction priors now allow non-reference weights above one via non-negative omega-scale priors or unrestricted log-omega priors - replaces the legacy dot-named weightfunction prior specifications with the unified weightfunction prior API and updates JAGS generation, marginal likelihood computation, posterior extraction, diagnostics, and summary tables to use the new component-local omega representation - composed selection-bias priors and publication-bias mixtures now support prior sampling and explicit unsupported-operation errors for ambiguous scalar prior generics Fixes - reports inclusion Bayes factors as NA when the prior assigns probability 0 or 1 to inclusion, while keeping finite-sample bounds for posterior inclusion probabilities of 0 or 1 - fixes incorrect ordering the printed mixture priors - fixes formula with no intercepts coded as 0 (instead of only -1) - fixes bug in .is.wholenumber with NAs and na.rm = TRUE - fixes ggplot prior spike layers for marginal factor plots with density and point components Changes in version 0.2.23 (2025-12-08) Fixes - JAGS_diagnostics functions now correctly handle factor parameters nested within mixture priors Changes in version 0.2.22 (2025-09-14) Fixes - plot_posterior() function with spike and slab priors Changes - unifies back-end of prior_mixture() and prior_spike_and_slab() Changes in version 0.2.21 (2025-08-29) Fixes - JAGS_formula() function now replaces removed missing intercept with 0 (so the model matrix remains unchanged) - resetting silent = FALSE argument in the JAGS_fit() function now fits the model non-silently again Changes in version 0.2.20 (2025-07-30) Features - extending prior functions to accept expression() instead of a parameter, such objects can be use to create prior distributions that depend on other parameters in JAGS - extending the formula interface of JAGS_fit() function to accept expressions that are appended as literal text to the generated JAGS formula - extending the formula interface of JAGS_fit() function to handle uncorrelated random effects via (x||y) (lme4-like) notation Fixes - JAGS_estimates_table not printing formula prefix when only spike and slab priors are supplied Changes in version 0.2.19 (2025-06-08) Features - adds max_extend option to autofit_control argument in JAGS_fit() to limit the number of iterations for the model extension - adds JASP progress bar integration Fixes - JAGS_diagnostics_density() plots for mixture distributions - prior and posterior plot_posterior() for simple as_mixed_posteriors objects - JAGS_evaluate_formula() for mixture and spike and slab priors - set Bayes factors based on alternative only prior distributions to NA - better handling of posterior samples in .fit_to_posterior() Changes in version 0.2.18 (2025-01-14) Features - adding prior_mixture() function for creating a mixture of prior distributions - adding as_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 output - adding interpret2() function for another way of creating textual summaries without the need of inference and samples objects - speedup and improvements to the runjags_estimates_table() function Fixes - small fixes for expansion of the RoBMA functionality version 0.2.17 Features - adding informed prior distributions for dichotomous and time to event outcomes based on Cochrane Database of Systematic Reviews to prior_informed() function - adding bridge object convenience function bridge_object() (fixes: https://github.com/FBartos/BayesTools/issues/28) - adding Na/NaN tests for check_ functions (fixes: https://github.com/FBartos/BayesTools/issues/26) Fixes - ability to run more than 4 chains (fixes: https://github.com/FBartos/BayesTools/issues/20) version 0.2.16 Features - update an existing JAGS fit with JAGS_extend() function - new element of the autofit_control argument in JAGS_fit(): "restarts" allows to restart model initialization up to restarts times in case of failure version 0.2.15 Fixes - fixing repeated print of previous prior distribution in model_summary_table() in case of prior_none() version 0.2.14 Features - adding contrast = "meandif" to the prior_factor function which generates identical prior distributions for difference between the grand mean and each factor level - adding contrast = "independent" to the prior_factor function which generates independent identical prior distributions for each factor level - remove_column function for removing columns from BayesTools_table objects without breaking the attributes etc... - adding empty table functions (https://github.com/FBartos/BayesTools/issues/10) - adding remove_parameters argument to model_summary_table() - adding multivariate point distribution functions - adding point prior distribution as option to prior_factor with "meandif" and "orthonormal" contrasts - adding marginal_posterior() function which creates marginal prior and posterior distributions (according to a model formula specification) - adding Savage_Dickey_BF() function to compute density ratio Bayes factors based on marginal_posterior objects - adding marginal_inference() function to combine information from marginal_posterior() and Savage_Dickey_BF() - adding marginal_estimates_table() function to summarize marginal_inference() objects - adding plot_marginal() function to visualize marginal_inference() objects Changes - contrast = "meandif" is now the default setting for prior_factor function - depreciating transform_orthonormal argument in favor of more general transform_factors argument - switching dummy contrast/factor attributes to treatment for consistency (https://github.com/FBartos/BayesTools/issues/23) Fixes - zero length inputs to check_bool(), check_char(), check_real(), check_int(), and check_list() do not throw error if allow_NULL = TRUE - properly aggregating identical priors in the plotting function (previously overlying multiple spikes on top of each other when attributes did not match) - student-t allowed as a prior distribution name - fixing factor contrast settings in JAGS_evaluate_formula - fixing spike prior transformations version 0.2.13 Features - runjags_estimates_table() function can now handle factor transformations - plot_posterior function can now handle factor transformations - ability to remove parameters from the runjags_estimates_table() function via the remove_parameters argument Fixes - inability to deal with constant intercept in marglik formula calculation - runjags_estimates_table() function can now remove factor spike prior distributions - marginal likelihood calculation for factor prior distributions with spike - mixing samples from vector priors of length 1 - same prior distributions not always combined together properly when part of them was generated via the formula interface version 0.2.12 Features - stan_estimates_summary() function - reducing dependency on runjags/rjags Fixes - dealing with posterior samples from rstan - dealing with vector posterior samples - fixing MCMC error of SD calculation for transformed samples (previously reported 100 times lower) version 0.2.11 Features - adding Bernoulli prior distribution - adding spike and slab type of prior distributions (without marginal likelihood computations/model-averaging capabilities) - new vignette comparing Bayes factor computation via marginal likelihood and spike and slab priors Fixes - when a transformation is applied, JAGS summary tables now produce the mean of the transformed variable (previous versions incorrectly returned transformation of the mean) Changes - runjags_XXX_table functions are now also exported as JAGS_XXX_functions for consistency with the rest of the code version 0.2.10 Features - trace, density, and autocorrelation diagnostic plots for JAGS models version 0.2.9 Fixes - dealing with NaNs in inclusion Bayes factors due to overflow with very large marginal likelihoods version 0.2.8 Fixes - dealing with point prior distributions in JAGS_marglik_parameters_formula function - posterior samples dropping name in runjags_estimates_table function - ensemble_summary_table and ensemble_diagnostics_table function can create table without model components version 0.2.7 Features - JAGS_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 syntax version 0.2.6 Features - plot_models implementation for factor predictors - format_parameter_names for cleaning parameter names from JAGS - mean, sd, and var functions now return the corresponding values for differences from the mean for the orthonormal prior distributions Fixes - proper splitting of transformed posterior samples based on orthonormal contrasts in runjags_summary_table function (previous version crashed under other than default fit_JAGS settings) - always showing name of the comparison group for treatment contrasts in runjags_summary_table function - better handling of transformed parameter names in plot_models function version 0.2.5 Features - add_column function for extending BayesTools_table objects without breaking the attributes etc... - ability to suppress the formula parameter prefix in BayesTools_table functions with with formula_prefix argument Fixes - allowing to pass point prior distributions for factor type predictors version 0.2.4 Features - adding possibility to multiply a (formula) prior parameter by another term (via multiply_by attribute passed with the prior) - t-test example vignette version 0.2.3 Fixes - fixing error from trying to rename formula parameters in BayesTools tables when multiple parameters were nested within a component version 0.2.2 Fixes - fixing layering of prior and posterior plots in plot_posterior (posterior is now plotted over the prior) version 0.2.1 Fixes - fixing JAGS code for multivariate-t prior distribution version 0.2.0 Changes - ensemble inference, summary, and plot functions now extract the prior list from attribute of the fit objects (previously, the prior_list needed to be passed for each model within the model_list as the priors argument Features - adding formula interface for fitting and computing marginal likelihood of JAGS models - adding factor prior distributions (with treatment and orthonormal contrasts) version 0.1.4 Fixes - fixing DOIs in the references file - adds marglik argument inclusion_BF to deal with over/underflow (Issue #9) - better passing of BF names through the ensemble_inference_table() (Issue #11) Features - adding logBF and BF01 options to ensemble_summary_table (Issue #7) version 0.1.3 Features - prior_informed function for creating informed prior distributions based on the past psychological and medical research version 0.1.2 Fixes - prior.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_parameters Features - interpret function for creating textual summaries based on inference and samples objects version 0.1.1 Fixes - plot_posterior fails with only mu & PET samples (Issue #5) - ordering by "probabilities" does not work in 'plot_models' (Issue #3) - BF goes to NaN when only a single model is present in 'models_inference' (Issue #2) - summary tables unit tests unable to deal with numerical precision - problems with aggregating samples across multiple spikes in `plot_posterior' Features - allow density.prior with range lower == upper (Issue #4) - moving rstan towards suggested packages version 0.1.0 - published on CRAN version 0.0.0.9010 - plotting functions for models version 0.0.0.9009 - plotting functions for posterior samples version 0.0.0.9008 - plotting functions for mixture of priors version 0.0.0.9007 - improvements to prior plotting functions version 0.0.0.9006 - ensemble and model summary tables functions version 0.0.0.9005 - posterior mixing functions version 0.0.0.9004 - model-averaging functions version 0.0.0.9003 - JAGS fitting related functions version 0.0.0.9002 - JAGS bridgesampling related functions version 0.0.0.9001 - JAGS model building related functions version 0.0.0.9000 - priors and related methods