NEWS.md
unupdate()
, a utility function to get Bayesian models un-fitted from the data, representing the priors only.describe_posterior()
for multiple-response models.rope()
for models with less than two parameters.mediation()
with stanmvreg
-models, which displays the wrong name for the response-value.effective_sample()
for models with only one parameter.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 FALSE
print()
-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
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-null
bayesfactor_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 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 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)NEWS.md
file to track changes to the package