NEWS

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BayesTools 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

BayesTools 0.2.23 (2025-12-08)

Fixes

• JAGS_diagnostics functions now correctly handle factor parameters nested within mixture priors

BayesTools 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()

BayesTools 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

BayesTools 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

BayesTools 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()

BayesTools 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