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.
|Depends:||quadprog, randtoolbox, parallel, R (≥ 2.15)|
|Maintainer:||Bjoern Bornkamp <bbnkmp at gmail.com>|
|License:||GPL-2 | GPL-3 [expanded from: GPL]|
|Citation:||iterLap citation info|
|CRAN checks:||iterLap results|
|Windows binaries:||r-devel: iterLap_1.1-2.zip, r-release: iterLap_1.1-2.zip, r-oldrel: iterLap_1.1-2.zip|
|OS X Mavericks binaries:||r-release: iterLap_1.1-2.tgz, r-oldrel: iterLap_1.1-2.tgz|
|Old sources:||iterLap archive|
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