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https://github.com/stan-dev/loo

loo R package for approximate leave-one-out cross-validation (LOO-CV) and Pareto smoothed importance sampling (PSIS)
https://github.com/stan-dev/loo

bayesian bayesian-data-analysis bayesian-inference bayesian-methods bayesian-statistics cross-validation information-criterion model-comparison r-package stan

Last synced: 3 months ago
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loo R package for approximate leave-one-out cross-validation (LOO-CV) and Pareto smoothed importance sampling (PSIS)

Host: GitHub
URL: https://github.com/stan-dev/loo
Owner: stan-dev
License: other
Created: 2015-06-14T05:08:55.000Z (about 9 years ago)
Default Branch: master
Last Pushed: 2024-03-29T19:32:46.000Z (3 months ago)
Last Synced: 2024-03-30T22:19:09.797Z (3 months ago)
Topics: bayesian, bayesian-data-analysis, bayesian-inference, bayesian-methods, bayesian-statistics, cross-validation, information-criterion, model-comparison, r-package, stan
Language: R
Homepage: https://mc-stan.org/loo
Size: 111 MB
Stars: 141
Watchers: 18
Forks: 35
Open Issues: 39
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE

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awesome-stars - stan-dev/loo - loo R package for approximate leave-one-out cross-validation (LOO-CV) and Pareto smoothed importance sampling (PSIS) (R)

README

        # loo 

[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/loo?color=blue)](https://cran.r-project.org/web/packages/loo)

[![RStudio_CRAN_mirror_downloads_badge](https://cranlogs.r-pkg.org/badges/loo?color=blue)](https://cran.r-project.org/web/packages/loo)

[![codecov](https://codecov.io/gh/stan-dev/loo/branch/master/graph/badge.svg)](https://codecov.io/github/stan-dev/loo?branch=master)

[![R-CMD-check](https://github.com/stan-dev/loo/workflows/R-CMD-check/badge.svg)](https://github.com/stan-dev/loo/actions)

### Efficient approximate leave-one-out cross-validation for fitted Bayesian models

__loo__ is an R package that allows users to compute efficient approximate

leave-one-out cross-validation for fitted Bayesian models, as well as model

weights that can be used to average predictive distributions. 

The __loo__ package package implements the fast and stable computations for 

approximate LOO-CV and WAIC from

* Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model 

evaluation using leave-one-out cross-validation and WAIC. 

_Statistics and Computing_. 27(5), 1413--1432. 

doi:10.1007/s11222-016-9696-4. [Online](https://link.springer.com/article/10.1007/s11222-016-9696-4), 

[arXiv preprint arXiv:1507.04544](https://arxiv.org/abs/1507.04544).

and computes model weights as described in

* Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2018). Using

stacking to average Bayesian predictive distributions. In Bayesian

Analysis, doi:10.1214/17-BA1091. 

[Online](https://projecteuclid.org/euclid.ba/1516093227),

[arXiv preprint arXiv:1704.02030](https://arxiv.org/abs/1704.02030).

From existing posterior simulation draws, we compute approximate LOO-CV using

Pareto smoothed importance sampling (PSIS), a new procedure for regularizing

importance weights. As a byproduct of our calculations, we also obtain

approximate standard errors for estimated predictive errors and for comparing

predictive errors between two models. We recommend PSIS-LOO-CV instead of WAIC, 

because PSIS provides useful diagnostics and effective sample size and Monte 

Carlo standard error estimates.

### Resources

* [mc-stan.org/loo](https://mc-stan.org/loo) (online documentation, vignettes)

* [Ask a question](https://discourse.mc-stan.org) (Stan Forums on Discourse)

* [Open an issue](https://github.com/stan-dev/loo/issues) (GitHub issues for bug reports, feature requests)

### Installation

* Install the latest release from CRAN:

```r

install.packages("loo")

```

* Install the latest development version from GitHub:

```r

# install.packages("remotes")

remotes::install_github("stan-dev/loo")

```

We do _not_ recommend setting `build_vignettes=TRUE` when installing from GitHub

because some of the vignettes take a long time to build and are always available

online at [mc-stan.org/loo/articles/](https://mc-stan.org/loo/articles/).

### Python and Matlab/Octave Code

Corresponding Python and Matlab/Octave code can be found at the

[avehtari/PSIS](https://github.com/avehtari/PSIS) repository.

### License

The code is distributed under the GPL 3 license. The documentation is distributed under the CC BY 4.0 license.