Added reference to paper published in JOSS, updated version number

CITATION.cff

@@ -0,0 +1,52 @@
+cff-version: "1.2.0"
+authors:
+- family-names: Stimper
+  given-names: Vincent
+  orcid: "https://orcid.org/0000-0002-4965-4297"
+- family-names: Liu
+  given-names: David
+- family-names: Campbell
+  given-names: Andrew
+- family-names: Berenz
+  given-names: Vincent
+- family-names: Ryll
+  given-names: Lukas
+- family-names: Schölkopf
+  given-names: Bernhard
+  orcid: "https://orcid.org/0000-0002-8177-0925"
+- family-names: Hernández-Lobato
+  given-names: José Miguel
+doi: 10.5281/zenodo.8027667
+message: If you use this software, please cite our article in the
+  Journal of Open Source Software.
+preferred-citation:
+  authors:
+  - family-names: Stimper
+    given-names: Vincent
+    orcid: "https://orcid.org/0000-0002-4965-4297"
+  - family-names: Liu
+    given-names: David
+  - family-names: Campbell
+    given-names: Andrew
+  - family-names: Berenz
+    given-names: Vincent
+  - family-names: Ryll
+    given-names: Lukas
+  - family-names: Schölkopf
+    given-names: Bernhard
+    orcid: "https://orcid.org/0000-0002-8177-0925"
+  - family-names: Hernández-Lobato
+    given-names: José Miguel
+  date-published: 2023-06-24
+  doi: 10.21105/joss.05361
+  issn: 2475-9066
+  issue: 86
+  journal: Journal of Open Source Software
+  publisher:
+    name: Open Journals
+  start: 5361
+  title: "normflows: A PyTorch Package for Normalizing Flows"
+  type: article
+  url: "https://joss.theoj.org/papers/10.21105/joss.05361"
+  volume: 8
+title: "normflows: A PyTorch Package for Normalizing Flows"

README.md

@@ -3,17 +3,17 @@
 [![documentation](https://github.com/VincentStimper/normalizing-flows/actions/workflows/mkdocs.yaml/badge.svg)](https://vincentstimper.github.io/normalizing-flows/)
 ![unit-tests](https://github.com/VincentStimper/normalizing-flows/actions/workflows/pytest.yaml/badge.svg)
 ![code coverage](https://raw.githubusercontent.com/VincentStimper/normalizing-flows/coverage-badge/coverage.svg?raw=true)
-[![License: MIT](https://img.shields.io/badge/Licence-MIT-lightgrey)](https://opensource.org/licenses/MIT)
-[![arXiv](https://img.shields.io/badge/arXiv-2302.12014-b31b1b.svg)](https://arxiv.org/abs/2302.12014) 
-[![PyPI](https://img.shields.io/badge/PyPI-1.7.0-blue.svg)](https://pypi.org/project/normflows/)
+[![License: MIT](https://img.shields.io/badge/Licence-MIT-b31b1b.svg)](https://opensource.org/licenses/MIT)
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.05361/status.svg)](https://doi.org/10.21105/joss.05361)
+[![PyPI](https://img.shields.io/badge/PyPI-1.7.1-blue.svg)](https://pypi.org/project/normflows/)
 [![Downloads](https://static.pepy.tech/personalized-badge/normflows?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads)](https://pepy.tech/project/normflows)
 
 
 `normflows` is a PyTorch implementation of discrete normalizing flows. Many popular flow architectures are implemented,
 see the [list below](#implemented-flows). The package can be easily [installed via pip](#installation).
 The basic usage is described [here](#usage), and a [full documentation](https://vincentstimper.github.io/normalizing-flows/) 
-is available as well. A more detailed description of this package is given in out accompanying 
-[paper](https://arxiv.org/abs/2302.12014).
+is available as well. A more detailed description of this package is given in our
+[accompanying paper](https://joss.theoj.org/papers/10.21105/joss.05361).
 
 Several sample use cases are provided in the 
 [`examples` folder](https://github.com/VincentStimper/normalizing-flows/blob/master/examples), 
@@ -175,7 +175,7 @@ as well, we apply a Neural Spline Flow model to a distribution defined on a cyli
 
 ![Neural Spline Flow applied to target distribution on a cylinder](https://raw.githubusercontent.com/VincentStimper/normalizing-flows/master/figures/nsf_cylinder_3d.png)
 
-This example is considered in the [paper](https://arxiv.org/abs/2302.12014) accompanying this repository.
+This example is considered in the [paper](https://joss.theoj.org/papers/10.21105/joss.05361) accompanying this repository.
 
 ## Support
 
@@ -228,20 +228,27 @@ has been build upon `normflows`.
 
 ## Citation
 
-If you use `normflows`, please consider citing the [corresponding paper](https://arxiv.org/abs/2302.12014) as follows.
+If you use `normflows`, please cite the 
+[corresponding paper](https://joss.theoj.org/papers/10.21105/joss.05361) as follows.
 
-> Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato.
-> normflows: A PyTorch Package for Normalizing Flows, arXiv preprint arXiv:2302.12014, 2023.
+> Stimper et al., (2023). normflows: A PyTorch Package for Normalizing Flows. 
+> Journal of Open Source Software, 8(86), 5361, https://doi.org/10.21105/joss.05361
 
 **Bibtex**
 
 ```
-@article{normflows,
-  author = {Vincent Stimper and David Liu and Andrew Campbell and Vincent Berenz and Lukas Ryll and Bernhard Sch{\"o}lkopf and Jos{\'e} Miguel Hern{\'a}ndez-Lobato},
-  title = {normflows: {A} {P}y{T}orch {P}ackage for {N}ormalizing {F}lows},
-  journal = {arXiv preprint arXiv:2302.12014},
+@article{Stimper2023, 
+  author = {Vincent Stimper and David Liu and Andrew Campbell and Vincent Berenz and Lukas Ryll and Bernhard Schölkopf and José Miguel Hernández-Lobato}, 
+  title = {normflows: A PyTorch Package for Normalizing Flows}, 
+  journal = {Journal of Open Source Software}, 
+  volume = {8},
+  number = {86}, 
+  pages = {5361}, 
+  publisher = {The Open Journal}, 
+  doi = {10.21105/joss.05361}, 
+  url = {https://doi.org/10.21105/joss.05361}, 
   year = {2023}
-}
+} 
 ```
 
 

examples/paper_example_nsf_colab.ipynb

@@ -21,7 +21,7 @@
    "id": "a3f13738",
    "metadata": {},
    "source": [
-    "This is the example we consider in our [paper](https://arxiv.org/abs/2302.12014) about the `normflows` package.\n",
+    "This is the example we consider in our [paper](https://joss.theoj.org/papers/10.21105/joss.05361) about the `normflows` package.\n",
     "\n",
     "We aim to approximate a distribution having as circular and a normal coordinate. To construct such a case, let $x$ be the normal (unbound) coordinate follow a standard normal distribution, i.e.\n",
     "$$ p(x) = \\frac{1}{\\sqrt{2\\pi}} e^{-\\frac{1}{2} x ^ 2}.$$\n",

normflows/__init__.py

@@ -10,4 +10,4 @@
 from . import sampling
 from .sampling import HAIS
 
-__version__ = "1.7.0"
+__version__ = "1.7.1"

setup.py

@@ -2,7 +2,7 @@
 from codecs import open
 from os import path
 
-__version__ = "1.7.0"
+__version__ = "1.7.1"
 
 here = path.abspath(path.dirname(__file__))