Package: NeuralEstimators 0.1.1
NeuralEstimators: Likelihood-Free Parameter Estimation using Neural Networks
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.
Authors:
NeuralEstimators_0.1.1.tar.gz
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NeuralEstimators_0.1.1.tgz(r-4.4-any)NeuralEstimators_0.1.1.tgz(r-4.3-any)
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NeuralEstimators.pdf |NeuralEstimators.html✨
NeuralEstimators/json (API)
# Install 'NeuralEstimators' in R: |
install.packages('NeuralEstimators', repos = c('https://mattsainsburydale.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 22 days agofrom:9ca523b8d1. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 04 2024 |
R-4.5-win | OK | Nov 04 2024 |
R-4.5-linux | OK | Nov 04 2024 |
R-4.4-win | OK | Nov 04 2024 |
R-4.4-mac | OK | Nov 04 2024 |
R-4.3-win | OK | Nov 04 2024 |
R-4.3-mac | OK | Nov 04 2024 |
Exports:assessbiasbootstrapencodedataestimateinitialise_estimatorloadstateloadweightsmapestimatemlestimateplotdistributionplotestimatesriskrmsesampleposteriorsavestatetanhlosstrain
Dependencies:JuliaConnectoRmagrittr
Introduction to NeuralEstimators
Rendered fromNeuralEstimators.html.asis
usingR.rsp::asis
on Nov 04 2024.Last update: 2024-11-03
Started: 2024-11-03
NeuralEstimators with Incomplete Gridded Data
Rendered fromNeuralEstimators_IncompleteData.html.asis
usingR.rsp::asis
on Nov 04 2024.Last update: 2024-11-03
Started: 2024-11-03
Readme and manuals
Help Manual
Help page | Topics |
---|---|
NeuralEstimators: Likelihood-Free Parameter Estimation using Neural Networks | NeuralEstimators-package NeuralEstimators |
assess a neural estimator | assess |
computes a Monte Carlo approximation of an estimator's bias | bias |
bootstrap | bootstrap |
encodedata | encodedata |
estimate | estimate |
Initialise a neural estimator | initialise_estimator |
load a saved state of a neural estimator | loadstate |
load a collection of saved weights of a neural estimator | loadweights |
Maximum a posteriori estimation | mapestimate |
Maximum likelihood estimation | mlestimate |
Plot the empirical sampling distribution of an estimator. | plotdistribution |
Plot estimates vs. true values. | plotestimates |
computes a Monte Carlo approximation of an estimator's Bayes risk | risk |
computes a Monte Carlo approximation of an estimator's root-mean-square error (RMSE) | rmse |
sampleposterior | sampleposterior |
save the state of a neural estimator | savestate |
tanhloss | tanhloss |
Train a neural estimator | train |