NeuralEstimators
0.2.1Likelihood-Free Parameter Estimation using Neural Networks
Overview
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.
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- NOTE r-devel-linux-x86_64-debian-gcc
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
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- Documented parameters
- 97%
- Return-value docs
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- References docs
- 0%
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Package metadata
- First published
- 2024-09-11
- Total releases
- 6 / 2 yrs
- License
- GPL (>= 2) OSI
- Download size
- 628 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet