Nothing
BiBMA
functionNoBMA
and NoBMA.reg()
functions as wrappers around RoBMA
RoBMA.reg()
functions for simpler specification of publication bias unadjusted Bayesian model-averaged meta-analysisupdate.RoBMA()
function (only non-converged models by default or all by setting extend_all = TRUE
)RoBMA.reg()
functionRoBMA.reg
models with summary_marginal()
and plot_marginal()
functionsweight
argument in the RoBMA
and combine_data
functions in order to pass custom
likelihood weightsweighted_type = "inverse_sqrt"
argument priors_hierarchical
and priors_hierarchical_null
arguments instead of priors_rho
and priors_rho_null
. The model summary now shows Hierarchical
component summary.study_ids
argument in RoBMA()
and setting weighted = TRUE
. The likelihood contribution of estimates from each study is down-weighted proportionally to the number of estimates in that study. Note that this experimental feature is supposed to provide a conservative alternative for estimating RoBMA in cases with multiple estimates from a study where the multivariate option is not computationally feasible.study_ids
argument in RoBMA
. However, note that this is (1) an experimental feature and (2) the computational expense of fitting selection models with clustering is extreme. As of now, it is almost impossible to have more than 2-3 estimates clustered within a single study).informed_prior()
function (from the BayesTools package) that allows specification of various informed prior distributions from the field of medicine and psychologysave = "min"
interpret()
function (issue #11)output_scale
argument to plot()
and plot_models()
functionsPlease notice that this is a major release that breaks backwards compatibility.
priors_mu
-> priors_effect
, priors_tau
-> priors_heterogeneity
, and priors_omega
-> priors_bias
),prior(distribution = "two.sided", parameters = ...)
-> prior_weightfunction(distribution = "two.sided", parameters = ...)
),prior_none()
),prior_PET(distribution = "Cauchy", parameters = ...)
and prior_PEESE(distribution = "Cauchy", parameters = ...)
),model_type
argument allowing to specify different "pre-canned" models ("PSMA"
= RoBMA-PSMA, "PP"
= RoBMA-PP, "2w"
= corresponding to Maier et al., in press , manuscript),combine_data
function allows combination of different effect sizes / variability measures into a common effect size measure (also used from within the RoBMA
function),autofit = FALSE
)prior_scale
and transformation
arguments),plot_models()
function and the forest plot can be obtained with the forest()
function,plot.RoBMA()
function and parameter = "weightfunction"
and parameter = "PET-PEESE"
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