BSL (0.1.1)

Bayesian Synthetic Likelihood with Graphical Lasso.

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 BSL and BSL with graphical lasso (BSLasso, An et al. (2018) ), which is computationally more efficient when the dimension of the summary statistic is high. Extensions to this package are planned.

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

License: GPL (>= 2)

Uses: coda, cvTools, foreach, ggplot2, glasso, gridExtra, MASS, elliplot

Released over 1 year ago.