NeuralEstimators: Likelihood-Free Parameter Estimation using Neural Networks
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.
| Version: |
0.1.0 |
| Imports: |
JuliaConnectoR, magrittr |
| Suggests: |
dplyr, ggplot2, ggplotify, ggpubr, gridExtra, knitr, rmarkdown, markdown, testthat (≥ 3.0.0) |
| Published: |
2024-09-11 |
| DOI: |
10.32614/CRAN.package.NeuralEstimators |
| Author: |
Matthew Sainsbury-Dale [aut, cre] |
| Maintainer: |
Matthew Sainsbury-Dale <msainsburydale at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| SystemRequirements: |
Julia (>= 1.9) |
| Citation: |
NeuralEstimators citation info |
| Materials: |
README |
| CRAN checks: |
NeuralEstimators results |
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