Packaged: 2020-08-07 06:02:05 UTC; Frantisek

ensembles use Bayes factors to test for the presence or absence of the

Suggests: ggplot2, parallel, rstan, metaBMA, testthat, vdiffr, knitr,

person("Joris", "Goosen",         role = "ctb"),
comment="Original copyright holder of some modified code where indicated."),
comment="Original copyright holder of some modified code where indicated.")
publication bias). The RoBMA framework uses Bayesian model-averaging to
combine the competing meta-analytic models into a model ensemble, weights
the posterior parameter distributions based on posterior model probabilities
and uses Bayes factors to test for the presence or absence of the
individual components (e.g., effect vs. no effect; Bartoš et al., 2021,
<doi:10.31234/osf.io/kvsp7>; Maier, Bartoš & Wagenmakers, in press,
<doi:10.31234/osf.io/u4cns>). Users can define a wide range of non-informative
or informative prior distributions for the effect size, heterogeneity,
and publication bias components (including selection models and PET-PEESE).
The package provides convenient functions for summary, visualizations, and
Imports: BayesTools, runjags, bridgesampling, rjags, coda, psych,
stats, graphics, extraDistr, scales, callr, Rdpack, ggplot2
Suggests: parallel, rstan, metaBMA, testthat, vdiffr, knitr, rmarkdown,
Matthew Denwood [cph] (Original copyright holder of some modified code
Martyn Plummer [cph] (Original copyright holder of some modified code

Imports: BayesTools (>= 0.1.2), runjags, bridgesampling, rjags, coda,
psych, stats, graphics, extraDistr, scales, callr, Rdpack,

Imports: BayesTools (>= 0.1.3), runjags, bridgesampling, rjags, coda,

Imports: BayesTools (>= 0.2.0), runjags, bridgesampling, rjags, coda,
psych, stats, graphics, extraDistr, mvtnorm, scales, callr,

SystemRequirements: JAGS >= 4.3.1 (https://mcmc-jags.sourceforge.io/)
psych, stats, graphics, extraDistr, mvtnorm, scales, Rdpack,

Title: Robust Bayesian Survival Analysis
person("František", "Bartoš", role = c("aut", "cre"),
person("Julia M.", "Haaf", role = "ths",
Description: A framework for estimating ensembles of parametric survival models
with different parametric families. The RoBSA framework uses Bayesian
model-averaging to combine the competing parametric survival models into
a model ensemble, weights the posterior parameter distributions based on
posterior model probabilities and uses Bayes factors to test for the
presence or absence of the individual predictors or preference for a
parametric family (Bartoš, Aust & Haaf, 2021, <doi:10.48550/arXiv.2112.08311>).
The user can define a wide range of informative priors for all parameters
of interest. The package provides convenient functions for summary, visualizations,
fit diagnostics, and prior distribution calibration.
Imports: BayesTools (>= 0.2.10), survival, rjags, runjags,
bridgesampling, scales, coda, stats, graphics, Rdpack
Suggests: parallel, ggplot2, flexsurv, testthat, vdiffr, knitr,