iterLap: Approximate Probability Densities by Iterated Laplace Approximations

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>
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
OS X binaries: r-release: iterLap_1.1-3.tgz, r-oldrel: iterLap_1.1-3.tgz
Old sources: iterLap archive


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