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: 1.1-4
Depends: quadprog, randtoolbox, parallel, R (≥ 2.15)
Published: 2023-09-30
DOI: 10.32614/CRAN.package.iterLap
Author: Bjoern Bornkamp
Maintainer: Bjoern Bornkamp <bbnkmp at>
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_1.1-4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): iterLap_1.1-4.tgz, r-oldrel (arm64): iterLap_1.1-4.tgz, r-release (x86_64): iterLap_1.1-4.tgz, r-oldrel (x86_64): iterLap_1.1-4.tgz
Old sources: iterLap archive


Please use the canonical form to link to this page.