- 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)

- ability to run more than 4 chains (fixes: https://github.com/FBartos/BayesTools/issues/20)

- 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

- fixing repeated print of previous prior distribution in
`model_summary_table()`

in case of`prior_none()`

- 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

`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)

- 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

`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

- 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

`stan_estimates_summary()`

function- reducing dependency on runjags/rjags

- 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)

- 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

- when a transformation is applied, JAGS summary tables now produce the mean of the transformed variable (previous versions incorrectly returned transformation of the mean)

- runjags_XXX_table functions are now also exported as JAGS_XXX_functions for consistency with the rest of the code

- trace, density, and autocorrelation diagnostic plots for JAGS models

- dealing with NaNs in inclusion Bayes factors due to overflow with very large marginal likelihoods

- 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

`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

`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

- 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

`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

- allowing to pass point prior distributions for factor type predictors

- adding possibility to multiply a (formula) prior parameter by another term (via
`multiply_by`

attribute passed with the prior) - t-test example vignette

- fixing error from trying to rename formula parameters in BayesTools tables when multiple parameters were nested within a component

- fixing layering of prior and posterior plots in
`plot_posterior`

(posterior is now plotted over the prior)

- fixing JAGS code for multivariate-t prior distribution

- 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

- adding formula interface for fitting and computing marginal likelihood of JAGS models
- adding factor prior distributions (with treatment and orthonormal contrasts)

- 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)

- adding logBF and BF01 options to
`ensemble_summary_table`

(Issue #7)

`prior_informed`

function for creating informed prior distributions based on the past psychological and medical research

`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`

`interpret`

function for creating textual summaries based on inference and samples objects

`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’

- allow density.prior with range lower == upper (Issue #4)
- moving rstan towards suggested packages

- published on CRAN

- plotting functions for models

- plotting functions for posterior samples

- plotting functions for mixture of priors

- improvements to prior plotting functions

- ensemble and model summary tables functions

- posterior mixing functions

- model-averaging functions

- JAGS fitting related functions

- JAGS bridgesampling related functions

- JAGS model building related functions

- priors and related methods