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iterLap  

Approximate Probability Densities by Iterated Laplace Approximations
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]
All associated links for this package
First Published: 2011-03-09
Latest Update: 2017-08-05
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 22 Dec. 2024.
Previous versions and publish date:
1.0-1 (2011-03-09 15:25), 1.0-2 (2011-10-23 17:40), 1.1-1 (2011-11-13 18:29), 1.1-2 (2012-05-22 22:52), 1.1-3 (2017-08-05 21:28)
Other packages that cited iterLap R package
View iterLap citation profile
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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