The default

`ci`

width has been changed to 0.95 instead of 0.89 (see here).Column names for

`bayesfactor_restricted()`

are now`p_prior`

and`p_posterior`

(was`Prior_prob`

and`Posterior_prob`

), to be consistent with`bayesfactor_inclusion()`

output.Removed the

`mhdior`

experimental function.

- Support for
`blavaan`

models. - Support for
`blrm`

models (*rmsb*). - Support for
`BGGM`

models (*BGGM*). `check_prior()`

and`describe_prior()`

should now also work for more ways of prior definition in models from*rstanarm*or*brms*.

- Fixed bug in
`print()`

method for the`mediation()`

function. - Fixed remaining inconsistencies with CI values, which were not reported as fraction for
`rope()`

. - Fixed issues with special prior definitions in
`check_prior()`

,`describe_prior()`

and`simulate_prior()`

.

- Support for
`bamlss`

models. - Roll-back R dependency to R >= 3.4.

- All
`.stanreg`

methods gain a`component`

argument, to also include auxiliary parameters.

`bayesfactor_parameters()`

no longer errors for no reason when computing extremely un/likely direction hypotheses.`bayesfactor_pointull()`

/`bf_pointull()`

are now`bayesfactor_pointnull()`

/`bf_pointnull()`

(can*you*spot the difference? #363 ).

`sexit()`

, a function for sequential effect existence and significance testing (SEXIT).

- Added startup-message to warn users that default ci-width might change in a future update.
- Added support for
*mcmc.list*objects.

`unupdate()`

gains a`newdata`

argument to work with`brmsfit_multiple`

models.- Fixed issue in Bayes factor vignette (don’t evaluate code chunks if packages not available).

- Added
`as.matrix()`

function for`bayesfactor_model`

arrays. `unupdate()`

, a utility function to get Bayesian models un-fitted from the data, representing the priors only.

`ci()`

supports`emmeans`

- both Bayesian and frequentist ( #312 - cross fix with`parameters`

)

- Fixed issue with
*default*rope range for`BayesFactor`

models. - Fixed issue in collinearity-check for
`rope()`

for models with less than two parameters. - Fixed issue in print-method for
`mediation()`

with`stanmvreg`

-models, which displays the wrong name for the response-value. - Fixed issue in
`effective_sample()`

for models with only one parameter. `rope_range()`

for`BayesFactor`

models returns non-`NA`

values ( #343 )

`mediation()`

, to compute average direct and average causal mediation effects of multivariate response models (`brmsfit`

,`stanmvreg`

).

`bayesfactor_parameters()`

works with`R<3.6.0`

.

- Preliminary support for
*stanfit*objects. - Added support for
*bayesQR*objects.

`weighted_posteriors()`

can now be used with data frames.- Revised
`print()`

for`describe_posterior()`

. - Improved value formatting for Bayesfactor functions.

- Link transformation are now taken into account for
`emmeans`

objets. E.g., in`describe_posterior()`

. - Fix
`diagnostic_posterior()`

when algorithm is not “sampling”. - Minor revisions to some documentations.
- Fix CRAN check issues for win-old-release.

`describe_posterior()`

now also works on`effectsize::standardize_posteriors()`

.`p_significance()`

now also works on`parameters::simulate_model()`

.`rope_range()`

supports more (frequentis) models.

- Fixed issue with
`plot()`

`data.frame`

-methods of`p_direction()`

and`equivalence_test()`

. - Fix check issues for forthcoming insight-update.

- Support for
*bcplm*objects (package**cplm**)

`estimate_density()`

now also works on grouped data frames.

- Fixed bug in
`weighted_posteriors()`

to properly weight Intercept-only`BFBayesFactor`

models. - Fixed bug in
`weighted_posteriors()`

when models have very low posterior probability ( #286 ). - Fixed bug in
`describe_posterior()`

,`rope()`

and`equivalence_test()`

for*brmsfit*models with monotonic effect. - Fixed issues related to latest changes in
`as.data.frame.brmsfit()`

from the*brms*package.

- Added
`p_pointnull()`

as an alias to`p_MAP()`

. - Added
`si()`

function to compute support intervals. - Added
`weighted_posteriors()`

for generating posterior samples averaged across models. - Added
`plot()`

-method for`p_significance()`

. `p_significance()`

now also works for*brmsfit*-objects.`estimate_density()`

now also works for*MCMCglmm*-objects.`equivalence_test()`

gets`effects`

and`component`

arguments for*stanreg*and*brmsfit*models, to print specific model components.- Support for
*mcmc*objects (package**coda**) - Provide more distributions via
`distribution()`

. - Added
`distribution_tweedie()`

. - Better handling of
`stanmvreg`

models for`describe_posterior()`

,`diagnostic_posterior()`

and`describe_prior()`

.

`point_estimate()`

: argument`centrality`

default value changed from ‘median’ to ‘all’.`p_rope()`

, previously as exploratory index, was renamed as`mhdior()`

(for*Max HDI inside/outside ROPE*), as`p_rope()`

will refer to`rope(..., ci = 1)`

( #258 )

- Fixed mistake in description of
`p_significance()`

. - Fixed error when computing BFs with
`emmGrid`

based on some non-linear models ( #260 ). - Fixed wrong output for percentage-values in
`print.equivalence_test()`

. - Fixed issue in
`describe_posterior()`

for`BFBayesFactor`

-objects with more than one model.

`convert_bayesian_to_frequentist()`

Convert (refit) Bayesian model as frequentist`distribution_binomial()`

for perfect binomial distributions`simulate_ttest()`

Simulate data with a mean difference`simulate_correlation()`

Simulate correlated datasets`p_significance()`

Compute the probability of Practical Significance (ps)`overlap()`

Compute overlap between two empirical distributions`estimate_density()`

:`method = "mixture"`

argument added for mixture density estimation

- Fixed bug in
`simulate_prior()`

for stanreg-models when`autoscale`

was set to`FALSE`

- revised
`print()`

-methods for functions like`rope()`

,`p_direction()`

,`describe_posterior()`

etc., in particular for model objects with random effects and/or zero-inflation component

`check_prior()`

to check if prior is informative`simulate_prior()`

to simulate model’s priors as distributions`distribution_gamma()`

to generate a (near-perfect or random) Gamma distribution`contr.bayes`

function for orthogonal factor coding (implementation from Singmann & Gronau’s`bfrms`

, used for proper prior estimation when factor have 3 levels or more. See Bayes factor vignette ## Changes to functionsAdded support for

`sim`

,`sim.merMod`

(from`arm::sim()`

) and`MCMCglmm`

-objects to many functions (like`hdi()`

,`ci()`

,`eti()`

,`rope()`

,`p_direction()`

,`point_estimate()`

, …)`describe_posterior()`

gets an`effects`

and`component`

argument, to include the description of posterior samples from random effects and/or zero-inflation component.More user-friendly warning for non-supported models in

`bayesfactor()`

-methods

- Fixed bug in
`bayesfactor_inclusion()`

where the same interaction sometimes appeared more than once (#223) - Fixed bug in
`describe_posterior()`

for*stanreg*models fitted with fullrank-algorithm

`rope_range()`

for binomial model has now a different default (-.18; .18 ; instead of -.055; .055)`rope()`

: returns a proportion (between 0 and 1) instead of a value between 0 and 100`p_direction()`

: returns a proportion (between 0.5 and 1) instead of a value between 50 and 100 (#168)`bayesfactor_savagedickey()`

:`hypothesis`

argument replaced by`null`

as part of the new`bayesfactor_parameters()`

function

`density_at()`

,`p_map()`

and`map_estimate()`

:`method`

argument added`rope()`

:`ci_method`

argument added`eti()`

: Computes equal-tailed intervals`reshape_ci()`

: Reshape CIs between wide/long`bayesfactor_parameters()`

: New function, replacing`bayesfactor_savagedickey()`

, allows for computing Bayes factors against a*point-null*or an*interval-null*`bayesfactor_restricted()`

: Function for computing Bayes factors for order restricted models

`bayesfactor_inclusion()`

now works with`R < 3.6`

.

`equivalence_test()`

: returns capitalized output (e.g.,`Rejected`

instead of`rejected`

)`describe_posterior.numeric()`

:`dispersion`

defaults to`FALSE`

for consistency with the other methods

`pd_to_p()`

and`p_to_pd()`

: Functions to convert between probability of direction (pd) and p-value- Support of
`emmGrid`

objects:`ci()`

,`rope()`

,`bayesfactor_savagedickey()`

,`describe_posterior()`

, …

- Improved tutorial 2

`describe_posterior()`

: Fixed column order restoration`bayesfactor_inclusion()`

: Inclusion BFs for matched models are more inline with JASP results.

- plotting functions now require the installation of the
`see`

package `estimate`

argument name in`describe_posterior()`

and`point_estimate()`

changed to`centrality`

`hdi()`

,`ci()`

,`rope()`

and`equivalence_test()`

default`ci`

to`0.89`

`rnorm_perfect()`

deprecated in favour of`distribution_normal()`

`map_estimate()`

now returns a single value instead of a dataframe and the`density`

parameter has been removed. The MAP density value is now accessible via`attributes(map_output)$MAP_density`

`describe_posterior()`

,`describe_prior()`

,`diagnostic_posterior()`

: added wrapper function`point_estimate()`

added function to compute point estimates`p_direction()`

: new argument`method`

to compute pd based on AUC`area_under_curve()`

: compute AUC`distribution()`

functions have been added`bayesfactor_savagedickey()`

,`bayesfactor_models()`

and`bayesfactor_inclusion()`

functions has been added- Started adding plotting methods (currently in the
`see`

package) for`p_direction()`

and`hdi()`

`probability_at()`

as alias for`density_at()`

`effective_sample()`

to return the effective sample size of Stan-models`mcse()`

to return the Monte Carlo standard error of Stan-models

- Improved documentation
- Improved testing
`p_direction()`

: improved printing`rope()`

for model-objects now returns the HDI values for all parameters as attribute in a consistent way- Changes legend-labels in
`plot.equivalence_test()`

to align plots with the output of the`print()`

-method (#78)

`hdi()`

returned multiple class attributes (#72)- Printing results from
`hdi()`

failed when`ci`

-argument had fractional parts for percentage values (e.g.`ci = .995`

). `plot.equivalence_test()`

did not work properly for*brms*-models (#76).

- CRAN initial publication and 0.1.0 release
- Added a
`NEWS.md`

file to track changes to the package