mediation(), to compute average direct and average causal mediation effects of multivariate response models (brmsfit, stanmvreg).bayesfactor_parameters() works with R<3.6.0.weighted_posteriors() can now be used with data frames.print() for describe_posterior().emmeans objets. E.g., in describe_posterior().diagnostic_posterior() when algorithm is not “sampling”.describe_posterior() now also works on effectsize::standardize_posteriors().p_significance() now also works on parameters::simulate_model().rope_range() supports more (frequentis) models.plot() data.frame-methods of p_direction() and equivalence_test().estimate_density() now also works on grouped data frames.weighted_posteriors() to properly weight Intercept-only BFBayesFactor models.weighted_posteriors() when models have very low posterior probability ( #286 ).describe_posterior(), rope() and equivalence_test() for brmsfit models with monotonic effect.as.data.frame.brmsfit() from the brms package.p_pointnull() as an alias to p_MAP().si() function to compute support intervals.weighted_posteriors() for generating posterior samples averaged across models.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.distribution().distribution_tweedie().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 )p_significance().emmGrid based on some non-linear models ( #260 ).print.equivalence_test().describe_posterior() for BFBayesFactor-objects with more than one model.convert_bayesian_to_frequentist() Convert (refit) Bayesian model as frequentistdistribution_binomial() for perfect binomial distributionssimulate_ttest() Simulate data with a mean differencesimulate_correlation() Simulate correlated datasetsp_significance() Compute the probability of Practical Significance (ps)overlap() Compute overlap between two empirical distributionsestimate_density(): method = "mixture" argument added for mixture density estimationsimulate_prior() for stanreg-models when autoscale was set to FALSEprint()-methods for functions like rope(), p_direction(), describe_posterior() etc., in particular for model objects with random effects and/or zero-inflation componentcheck_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 functions
Added 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
bayesfactor_inclusion() where the same interaction sometimes appeared more than once (#223)describe_posterior() for stanreg models fitted with fullrank-algorithmrope_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 100p_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() functiondensity_at(), p_map() and map_estimate(): method argument addedrope(): ci_method argument addedeti(): Computes equal-tailed intervalsreshape_ci(): Reshape CIs between wide/longbayesfactor_parameters(): New function, replacing bayesfactor_savagedickey(), allows for computing Bayes factors against a point-null or an interval-nullbayesfactor_restricted(): Function for computing Bayes factors for order restricted modelsbayesfactor_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 methodspd_to_p() and p_to_pd(): Functions to convert between probability of direction (pd) and p-valueemmGrid objects: ci(), rope(), bayesfactor_savagedickey(), describe_posterior(), …describe_posterior(): Fixed column order restorationbayesfactor_inclusion(): Inclusion BFs for matched models are more inline with JASP results.see packageestimate argument name in describe_posterior() and point_estimate() changed to centralityhdi(), ci(), rope() and equivalence_test() default ci to 0.89rnorm_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_densitydescribe_posterior(), describe_prior(), diagnostic_posterior(): added wrapper functionpoint_estimate() added function to compute point estimatesp_direction(): new argument method to compute pd based on AUCarea_under_curve(): compute AUCdistribution() functions have been addedbayesfactor_savagedickey(), bayesfactor_models() and bayesfactor_inclusion() functions has been addedsee package) for p_direction() and hdi()probability_at() as alias for density_at()effective_sample() to return the effective sample size of Stan-modelsmcse() to return the Monte Carlo standard error of Stan-modelsp_direction(): improved printingrope() for model-objects now returns the HDI values for all parameters as attribute in a consistent wayplot.equivalence_test() to align plots with the output of the print()-method (#78)hdi() returned multiple class attributes (#72)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).NEWS.md file to track changes to the package