BSL: Bayesian Synthetic Likelihood

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 three methods (BSL, uBSL and semiBSL) and two shrinkage estimations (graphical lasso and Warton's estimation). uBSL (Price et al. (2018) <doi:10.1080/10618600.2017.1302882>) uses 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) <doi:10.1080/10618600.2018.1537928>). Extensions to this package are planned.

Version:	3.0.0
Depends:	R (≥ 3.4.0)
Imports:	glasso, ggplot2, MASS, mvtnorm, copula, graphics, gridExtra, foreach, coda, Rcpp, methods
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	elliplot, doParallel
Published:	2019-07-10
Author:	Ziwen An [aut, cre], Leah F. South [aut], Christopher C. Drovandi [aut]
Maintainer:	Ziwen An <ziwen.an at hdr.qut.edu.au>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
Materials:	NEWS
CRAN checks:	BSL results

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