Other packages > Find by keyword >

NeuralEstimators  

Likelihood-Free Parameter Estimation using Neural Networks
View on CRAN: Click here


Download and install NeuralEstimators package within the R console
Install from CRAN:
install.packages("NeuralEstimators")

Install from Github:
library("remotes")
install_github("cran/NeuralEstimators")

Install by package version:
library("remotes")
install_version("NeuralEstimators", "0.2.2")



Attach the package and use:
library("NeuralEstimators")
Maintained by
Matthew Sainsbury-Dale
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-09-11
Latest Update: 2025-03-02
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.
How to cite:
Matthew Sainsbury-Dale (2024). NeuralEstimators: Likelihood-Free Parameter Estimation using Neural Networks. R package version 0.2.2, https://cran.r-project.org/web/packages/NeuralEstimators. Accessed 12 Oct. 2026.
Previous versions and publish date:
0.1.0 (2024-09-11 19:20), 0.1.1 (2024-11-03 09:30), 0.1.2 (2024-12-19 16:00), 0.1.3 (2025-01-14 14:40), 0.2.0 (2025-03-02 12:10), 0.2.1 (2026-04-28 12:10), (2026-09-09 08:30)
Other packages that cited NeuralEstimators R package
View NeuralEstimators citation profile
Other R packages that NeuralEstimators depends, imports, suggests or enhances
Complete documentation for NeuralEstimators
Functions, R codes and Examples using the NeuralEstimators R package
Full NeuralEstimators package functions and examples
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

cglasso  
Conditional Graphical LASSO for Gaussian Graphical Models with Censored and Missing Values
Conditional graphical lasso estimator is an extension of the graphical lasso proposed to estimate th ...
Download / Learn more Package Citations See dependency  
gwavr  
Get Water Attributes Visually in R
Provides methods to Get Water Attributes Visually in R ('gwavr'). This allows the user to point and ...
Download / Learn more Package Citations See dependency  
SLEMI  
Statistical Learning Based Estimation of Mutual Information
The implementation of the algorithm for estimation of mutual information and channel capacity from e ...
Download / Learn more Package Citations See dependency  
nextGenShinyApps  
Craft Exceptional 'R Shiny' Applications and Dashboards with Novel Responsive Tools
Nove responsive tools for designing and developing 'Shiny' dashboards and applications. The scripts ...
Download / Learn more Package Citations See dependency  
ggparliament  
Parliament Plots
Simple parliament plots using 'ggplot2'. Visualize election results as points in the architectural l ...
Download / Learn more Package Citations See dependency  

29,000

R Packages

247,686

Dependencies

76,729

Author Associations

29,001

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA