Package: NeuralEstimators
Title: Likelihood-Free Parameter Estimation using Neural Networks
Version: 0.2.1
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 Bayes estimators, which are neural networks that map data to a point summary of the posterior distribution (Sainsbury-Dale et al., 2024, <doi:10.1080/00031305.2023.2249522>). These estimators are likelihood-free and amortised, in the sense that, once the neural networks are trained on simulated data, inference from observed data can be made in a fraction of the time required by conventional approaches. The package also supports amortised Bayesian or frequentist inference using neural networks that approximate the posterior or likelihood-to-evidence ratio (Zammit-Mangion et al., 2025, Sec. 3.2, 5.2, <doi:10.48550/arXiv.2404.12484>). The package accommodates any model for which simulation is feasible by allowing users to define models implicitly 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 (>= 1.11)
VignetteBuilder: R.rsp
URL: https://github.com/msainsburydale/NeuralEstimators,
        https://msainsburydale.github.io/NeuralEstimators.jl/dev/
NeedsCompilation: no
Packaged: 2026-04-28 08:35:20 UTC; sainsbmd
Author: Matthew Sainsbury-Dale [aut, cre]
Repository: CRAN
Date/Publication: 2026-04-28 10:10:02 UTC
