Bayesian Survival Model using Generalized Weibull Regression
Bayesian survival model using Weibull regression on both scale and shape parameters.

Bayesian Synthetic Likelihood with Graphical Lasso
c(person("Ziwen","An",role=c("aut","cre"),email="ziwen.an@hdr.qut.edu.au",comment = c(ORCID = "0000-0002-9947-5182")),
person("Christopher C.","Drovandi",role=c("ctb"),email="c.drovandi@qut.edu.au",comment = c(ORCID = "0000-0001-9222-8763")),
person("Leah F.","South",role=c("ctb"),email="leah.south@hdr.qut.edu.au",comment = c(ORCID = "0000-0002-5646-2963")))
Bayesian synthetic likelihood (BSL, Price et al. (2018) <doi:10.1080/10618600.2017.1302882>)
is an alternative to standard, non-parametric approximate Bayesian computation (ABC). BSL assumes a
multivariate normal distribution for the summary statistic likelihood and it is suitable when
the distribution of the model summary statistics is sufficiently regular. This package provides
a Metropolis Hastings Markov chain Monte Carlo implementation of BSL and
BSL with graphical lasso (BSLasso, An et al. (2018) <https://eprints.qut.edu.au/102263/>),
which is computationally more efficient when the dimension of the summary statistic is high.
Extensions to this package are planned.
glasso, ggplot2, stats, MASS, cvTools, grid, gridExtra,
foreach, coda, utils

c(person("Ziwen","An",role=c("aut","cre"),email="ziwen.an@hdr.qut.edu.au",comment = c(ORCID = "0000-0002-9947-5182")),
person("Leah F.","South",role=c("aut"),email="leah.south@hdr.qut.edu.au",comment = c(ORCID = "0000-0002-5646-2963")),
person("Christopher C.","Drovandi",role=c("aut"),email="c.drovandi@qut.edu.au",comment = c(ORCID = "0000-0001-9222-8763")))
Bayesian synthetic likelihood (BSL, Price et al. (2018) <doi:10.1080/10618600.2017.1302882>)
is an alternative to standard, non-parametric approximate Bayesian
computation (ABC). BSL assumes a multivariate normal distribution
for the summary statistic likelihood and it is suitable when the
distribution of the model summary statistics is sufficiently regular.
This package provides a Metropolis Hastings Markov chain Monte Carlo
implementation of BSL, BSLasso and semiBSL. BSL with graphical lasso
(BSLasso, An et al. (2018) <https://eprints.qut.edu.au/102263/>) is
computationally more efficient when the dimension of the summary statistic
is high. A semi-parametric version of BSL (semiBSL, An et al. (2018)
<arXiv:1809.05800>) is more robust to non-normal summary
statistics. Extensions to this package are planned.
glasso, ggplot2, MASS, mvtnorm, copula, cvTools, graphics,
gridExtra, foreach, coda, Rcpp, methods
'BSL_package.R' 'RcppExports.R' 'S3_penbsl.R' 'S4Functions.R'
'S4.R' 'bsl.R' 'cell.R' 'combinePlotsBSL.R' 'covWarton.R'
'gaussianSynLike.R' 'glasso_cv.R' 'imports.R' 'kernelCDF.R'
'logitTransform.R' 'ma2.R' 'multignk.R' 'selectPenalty.R'
'semiparaKernelEstimate.R'