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https://github.com/turinglang/paretosmooth.jl

An implementation of PSIS algorithms in Julia.
https://github.com/turinglang/paretosmooth.jl

julia

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An implementation of PSIS algorithms in Julia.

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# ParetoSmooth

[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://TuringLang.github.io/ParetoSmooth.jl/stable)
[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://TuringLang.github.io/ParetoSmooth.jl/dev)
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[![Coverage](https://codecov.io/gh/TuringLang/ParetoSmooth.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/TuringLang/ParetoSmooth.jl)
[![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle)
[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)

ParetoSmooth.jl is a Julia package for efficient approximate leave-one-out cross-validation for fitted Bayesian models. We compute LOO-CV using Pareto smoothed importance sampling (PSIS), a modification of importance sampling. More details can be found in Vehtari, Gelman, and Gabry (2017).

If you use this library, please remember to cite both:
```
@misc{ParetoSmooth.jl,
author = {Carlos Parada },
title = {ParetoSmooth.jl},
url = {https://github.com/TuringLang/ParetoSmooth.jl},
version = {v0.7.1},
year = {2021},
month = {6}
}
```
and:
```
@Article{Vehtari2017,
author={Vehtari, Aki
and Gelman, Andrew
and Gabry, Jonah},
title={Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC},
journal={Statistics and Computing},
year={2017},
month={Sep},
day={01},
volume={27},
number={5},
pages={1413-1432},
issn={1573-1375},
doi={10.1007/s11222-016-9696-4},
url={https://doi.org/10.1007/s11222-016-9696-4}
}
```