NeuralEstimators

This repository contains the R interface to the Julia package NeuralEstimators. 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. It also facilitates the construction of neural networks that approximate the likelihood-to-evidence ratio in an amortised fashion, which allows for making inference based on the likelihood function or the entire posterior distribution. The package caters for any model for which simulation is feasible by allowing the user to implicitly define their model via simulated data. See the Julia documentation or the vignette to get started!

Installation

To install the package, please:

1. Install required software
   Ensure you have both Julia and R installed on your system.

2. Install the Julia version of NeuralEstimators

   • To install the stable version of the package, run the following command in your terminal:

     julia -e 'using Pkg; Pkg.add("NeuralEstimators")'

   • To install the development version, run:

     julia -e 'using Pkg; Pkg.add(url="https://github.com/msainsburydale/NeuralEstimators.jl")'

3. Install the R interface to NeuralEstimators

   • To install from CRAN, run the following command in R:

     install.packages("NeuralEstimators")

   • To install the development version, first ensure you have devtools installed, then run:

     devtools::install_github("msainsburydale/NeuralEstimators")

Supporting and citing

This software was developed as part of academic research. If you would like to support it, please star the repository. If you use the software in your research or other activities, please use the citation information accessible with the command:

citation("NeuralEstimators")

Contributing

If you find a bug or have a suggestion, please open an issue. For instructions for developing vignettes, see vignettes/README.md.

Papers using NeuralEstimators

• Likelihood-free parameter estimation with neural Bayes estimators [paper] [code]

• Neural Bayes estimators for censored inference with peaks-over-threshold models [paper]

• Neural Bayes estimators for irregular spatial data using graph neural networks [paper][code]

• Neural Methods for Amortized Inference [paper][code]

• Neural parameter estimation with incomplete data [paper][code]