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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 mail.de>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: yes
Citation: iterLap citation info
In views: Bayesian
CRAN checks: iterLap results

Documentation:

Reference manual: iterLap.pdf

Downloads:

Package source: iterLap_1.1-4.tar.gz
Windows binaries: r-devel: iterLap_1.1-4.zip, r-release: iterLap_1.1-4.zip, r-oldrel: iterLap_1.1-4.zip
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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=iterLap to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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