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 |

Reference manual: | BSL.pdf |

Package source: | BSL_3.0.0.tar.gz |

Windows binaries: | r-devel: BSL_3.0.0.zip, r-release: BSL_3.0.0.zip, r-oldrel: BSL_3.0.0.zip |

OS X binaries: | r-release: BSL_3.0.0.tgz, r-oldrel: BSL_3.0.0.tgz |

Old sources: | BSL archive |

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