BSL (3.0.0)

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Bayesian Synthetic Likelihood.

Bayesian synthetic likelihood (BSL, Price et al. (2018) ) 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) ) uses an unbiased estimator to the normal density. A semi-parametric version of BSL (semiBSL, An et al. (2018) ) 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) ). Extensions to this package are planned.

Maintainer: Ziwen An
Author(s): Ziwen An [aut, cre] (<>), Leah F. South [aut] (<>), Christopher C. Drovandi [aut] (<>)

License: GPL (>= 2)

Uses: coda, copula, foreach, ggplot2, glasso, gridExtra, MASS, mvtnorm, Rcpp, doParallel, elliplot

Released 5 months ago.

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