Package: NeuralEstimators
Title: Likelihood-Free Parameter Estimation using Neural Networks
Version: 0.1.1
Authors@R: 
    person(given = "Matthew",
           family = "Sainsbury-Dale",
           role = c("aut", "cre"),
           email = "msainsburydale@gmail.com")
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.
Maintainer: Matthew Sainsbury-Dale <msainsburydale@gmail.com>
License: GPL (>= 2)
Encoding: UTF-8
RoxygenNote: 7.3.2
Imports: JuliaConnectoR, magrittr
Suggests: dplyr, ggplot2, ggplotify, ggpubr, gridExtra, knitr,
        rmarkdown, markdown, R.rsp, testthat (>= 3.0.0)
Config/testthat/edition: 3
SystemRequirements: Julia (>= 1.9)
VignetteBuilder: R.rsp
NeedsCompilation: no
Packaged: 2024-11-03 07:21:57 UTC; matt
Author: Matthew Sainsbury-Dale [aut, cre]
Repository: CRAN
Date/Publication: 2024-11-03 08:30:02 UTC
