Provides functionality to perform a likelihood-free method for estimating the parameters of complex models that results in a simulated sample from the posterior distribution of model parameters given targets. The method begins with a accept/reject approximate bayes computation (ABC) step applied to a sample of points from the prior distribution of model parameters. Accepted points result in model predictions that are within the initially specified tolerance intervals around the target points. The sample is iteratively updated by drawing additional points from a mixture of multivariate normal distributions, accepting points within tolerance intervals. As the algorithm proceeds, the acceptance intervals are narrowed. The algorithm returns a set of points and sampling weights that account for the adaptive sampling scheme. For more details see Rutter, Ozik, DeYoreo, and Collier (2018) <doi:10.48550/arXiv.1804.02090>.
Version: 1.0.0 Depends: R (≥ 3.2.0) Imports: MASS, data.table, foreach, parallel, truncnorm, lhs, methods, stats, utils Published: 2021-04-12 DOI: 10.32614/CRAN.package.imabc Author: Christopher, E. Maerzluft [aut, cre], Carolyn Rutter [aut, cph], Jonathan Ozik [aut], Nicholson Collier [aut] Maintainer: "Christopher, E. Maerzluft" <cmaerzlu at rand.org> BugReports: https://github.com/carolyner/imabc/issues License: GPL-3 URL: https://github.com/carolyner/imabc NeedsCompilation: no Materials: README CRAN checks: imabc results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=imabc to link to this page.
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