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")))
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.
Imports: glasso, ggplot2, MASS, mvtnorm, copula, cvTools, graphics,
Collate: 'BSL_package.R' 'RcppExports.R' 'S3_penbsl.R' 'S4Functions.R'
'S4.R' 'bsl.R' 'cell.R' 'combinePlotsBSL.R' 'covWarton.R'
Christopher C. Drovandi [aut] (<https://orcid.org/0000-0001-9222-8763>)

implementation of three methods (BSL, uBSL and semiBSL) and two
shrinkage estimations (graphical lasso and Warton's estimation).
an unbiased estimator to the normal density. A semi-parametric version
of BSL (semiBSL, An et al. (2018) <arXiv:1809.05800>) is more robust
to non-normal summary statistics. Shrinkage estimations can help to
bring down the number of simulations when the dimension of the summary
statistic is high (e.g., BSLasso, An et al. (2019)
Imports: glasso, ggplot2, MASS, mvtnorm, copula, graphics, gridExtra,
Collate: 'BSL_model.R' 'BSL_package.R' 'RcppExports.R' 'S3_penbsl.R'
'S4Functions.R' 'S4.R' 'bsl.R' 'cell.R' 'combinePlotsBSL.R'

implementation of four methods (BSL, uBSL, semiBSL and BSLmisspec) and two
shrinkage estimators (graphical lasso and Warton's estimator).
to non-normal summary statistics. BSLmisspec (Frazier et al. 2019
<arXiv:1904.04551>) estimates the Gaussian synthetic likelihood whilst
acknowledging that there may be incompatibility between the model and the
observed summary statistic. Shrinkage estimation can help to decrease the
number of model simulations when the dimension of the summary statistic is
Imports: glasso, ggplot2, MASS, mvtnorm, copula, whitening, graphics,
gridExtra, foreach, coda, Rcpp, doRNG, methods, stringr, Rdpack
Suggests: elliplot, doParallel, rbenchmark, mixtools
Collate: 'BSL-package.R' 'RcppExports.R' 's4-MODEL.R' 's4-BSL.R'