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.
Version: 0.2.0 Imports: JuliaConnectoR, magrittr Suggests: dplyr, ggplot2, ggplotify, ggpubr, gridExtra, knitr, rmarkdown, markdown, R.rsp, testthat (≥ 3.0.0) Published: 2025-03-02 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)] URL: https://github.com/msainsburydale/NeuralEstimators, https://msainsburydale.github.io/NeuralEstimators.jl/dev/ NeedsCompilation: no SystemRequirements: Julia (>= 1.11) Citation: NeuralEstimators citation info Materials: README CRAN checks: NeuralEstimators results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=NeuralEstimators to link to this page.
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