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iterLap
View on CRAN: Click
here
Download and install iterLap package within the R console
Install from CRAN:
install.packages("iterLap")
Install from Github:
library("remotes")
install_github("cran/iterLap") Install by package version:
library("remotes")
install_version("iterLap", "1.1-4") Attach the package and use:
library("iterLap")
Maintained by
Bjoern Bornkamp
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2011-03-09
Latest Update: 2023-09-30
Description:
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.
How to cite:
Bjoern Bornkamp (2011). iterLap: Approximate Probability Densities by Iterated Laplace Approximations. R package version 1.1-4, https://cran.r-project.org/web/packages/iterLap. Accessed 05 Jun. 2026.
Previous versions and publish date:
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Other R packages that iterLap depends,
imports, suggests or enhances
Complete documentation for iterLap
Functions, R codes and Examples using
the iterLap R package
Some associated functions: GRApprox . ISandIMH . iterLap-internal . iterLap-package . iterLap . resample .
Some associated R codes: iterLap.R . Full iterLap package functions and examples
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