imabc: Incremental Mixture Approximate Bayesian Computation (IMABC)
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:
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