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NeuralEstimators  

Likelihood-Free Parameter Estimation using Neural Networks
View on CRAN: Click here


Download and install NeuralEstimators package within the R console
Install from CRAN:
install.packages("NeuralEstimators")

Install from Github:
library("remotes")
install_github("cran/NeuralEstimators")

Install by package version:
library("remotes")
install_version("NeuralEstimators", "0.1.1")



Attach the package and use:
library("NeuralEstimators")
Maintained by
Matthew Sainsbury-Dale
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-09-11
Latest Update: 2024-09-11
Description:
An 'R' interface to the 'Julia' package 'NeuralEstimators.jl'. The package facilitates the user-friendly development of neural point estimators, which are neural networks that map data to a point summary of the posterior distribution. These estimators are likelihood-free and amortised, in the sense that, after an initial setup cost, inference from observed data can be made in a fraction of the time required by conventional approaches; see Sainsbury-Dale, Zammit-Mangion, and Huser (2024) <doi:10.1080/00031305.2023.2249522> for further details and an accessible introduction. The package also enables the construction of neural networks that approximate the likelihood-to-evidence ratio in an amortised manner, allowing one to perform inference based on the likelihood function or the entire posterior distribution; see Zammit-Mangion, Sainsbury-Dale, and Huser (2024, Sec. 5.2) <doi:10.48550/arXiv.2404.12484>, and the references therein. The package accommodates any model for which simulation is feasible by allowing the user to implicitly define their model through simulated data.
How to cite:
Matthew Sainsbury-Dale (2024). NeuralEstimators: Likelihood-Free Parameter Estimation using Neural Networks. R package version 0.1.1, https://cran.r-project.org/web/packages/NeuralEstimators. Accessed 21 Nov. 2024.
Previous versions and publish date:
0.1.0 (2024-09-11 19:20)
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Functions, R codes and Examples using the NeuralEstimators R package
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