The iterLap (iterated Laplace approximation) algorithm approximates a general (possibly non-normalized) probability density on R^p, by repeated Laplace approximations to the difference between current approximation and true density (on log scale). The final approximation is a mixture of multivariate normal distributions and might be used for example as a proposal distribution for importance sampling (eg in Bayesian applications). The algorithm can be seen as a computational generalization of the Laplace approximation suitable for skew or multimodal densities.
| Version: | 1.1-3 |
| Depends: | quadprog, randtoolbox, parallel, R (≥ 2.15) |
| Published: | 2017-08-05 |
| Author: | Bjoern Bornkamp |
| Maintainer: | Bjoern Bornkamp <bbnkmp at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: | yes |
| Citation: | iterLap citation info |
| In views: | Bayesian |
| CRAN checks: | iterLap results |
| Reference manual: | iterLap.pdf |
| Package source: | iterLap_1.1-3.tar.gz |
| Windows binaries: | r-devel: iterLap_1.1-3.zip, r-release: iterLap_1.1-3.zip, r-oldrel: iterLap_1.1-3.zip |
| macOS binaries: | r-release: iterLap_1.1-3.tgz, r-oldrel: iterLap_1.1-3.tgz |
| Old sources: | iterLap archive |
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