BSL: Bayesian Synthetic Likelihood with Graphical Lasso

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 BSL and BSL with graphical lasso (BSLasso, An et al. (2018) <https://eprints.qut.edu.au/102263/>), which is computationally more efficient when the dimension of the summary statistic is high. Extensions to this package are planned.

Version: 0.1.1
Imports: glasso, ggplot2, stats, MASS, cvTools, grid, gridExtra, foreach, coda, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: elliplot
Published: 2018-03-23
Author: Ziwen An ORCID iD [aut, cre], Christopher C. Drovandi ORCID iD [ctb], Leah F. South ORCID iD [ctb]
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

Downloads:

Reference manual: BSL.pdf
Package source: BSL_0.1.1.tar.gz
Windows binaries: r-devel: BSL_0.1.1.zip, r-release: BSL_0.1.1.zip, r-oldrel: BSL_0.1.1.zip
OS X binaries: r-release: BSL_0.1.1.tgz, r-oldrel: not available

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