diff --git a/joss/paper.bib b/joss/paper.bib
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-@phdthesis{eudey1949,
-  title={\href{https://search.library.berkeley.edu/permalink/01UCS_BER/1thfj9n/alma991076184199706532}{On the Treatment of Discontinuous Random Variables}},
-  author={Eudey, Mark W},
-  year={1949},
-  school={University of California - Berkeley}
-}
-
-
-@book{lehmann_textbook,
-  title={\href{https://link.springer.com/book/10.1007/978-3-030-70578-7}{Testing Statistical Hypotheses}},
-  author={Lehmann, Erich Leo and Romano, Joseph P},
-  volume={4},
-  year={2022},
-  publisher={Springer}
-}
-
-
-@article{clopper_pearson,
-  title={\href{https://www.jstor.org/stable/2331986}{The Use of Confidence or Fiducial Limits Illustrated in the Case of the Binomial}},
-  author={Clopper, Charles J and Pearson, Egon S},
-  journal={Biometrika},
-  volume={26},
-  number={4},
-  pages={404--413},
-  year={1934},
-  publisher={JSTOR}
-}
-
-
-@article{vincent2024generalizable,
-  title={\href{https://arxiv.org/abs/2405.05439}{How Generalizable Is My Behavior Cloning Policy? A Statistical Approach to Trustworthy Performance Evaluation}},
-  author={Vincent, Joseph A and Nishimura, Haruki and Itkina, Masha and Shah, Paarth and Schwager, Mac and Kollar, Thomas},
-  journal={arXiv preprint arXiv:2405.05439},
-  year={2024}
-}
diff --git a/joss/paper.md b/joss/paper.md
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----
-title: 'binomial_cis: A Python Package for Optimal Binomial Confidence Intervals'
-tags:
-  - Python
-  - statistics
-  - confidence intervals
-  - binomial
-authors:
-  - name: Joseph A. Vincent
-    orcid: 0000-0002-2270-7395
-    affiliation: 1
-affiliations:
- - name: Department of Aeronautics and Astronautics, Stanford University
-   index: 1
-date: 11 June 2024
-bibliography: paper.bib
----
-
-
-# Summary
-[binomial_cis](https://github.com/TRI-ML/binomial_cis) is a Python package for computing confidence intervals for the probability of success parameter, $p$, of a binomial distribution. The binomial distribution represents the likelihood of observing $k$ successes in $n$ trials where the probability of success for each trial is $p$. For example, $p$ may be the probability of a coin flip landing on heads, and $k$ the number of heads we observe after $n$ flips. One often does not know the value of $p$ and wishes to estimate it. A confidence interval is a set, constructed based on $k, n$, that covers the unknown parameter $p$ with some user-specified probability. The binomial_cis package computes confidence intervals that lower and/or upper bound $p$ with a user-specified probability. 
-
-
-
-# Statement of Need
-
-Constructing confidence intervals for an unknown probability success given samples of successes and failures is one of the most fundamental problems in statistical inference.
-Research into this question dates back at least to the 1930s with the work of Clopper and Pearson [@clopper_pearson].
-A foundational result for constructing binomial confidence intervals of minimal width was given by [@eudey1949] and is formalized in [@lehmann_textbook].
-We refer to these intervals as *optimal binomial confidence intervals* and they have the property of being uniformly most accurate (UMA) and uniformly most accurate unbiased (UMAU).
-Practically, these intervals can provide better inference of $p$ at small sample sizes ($n \le 50$).
-The binomial_cis package is the first open-source implementation of these optimal binomial confidence intervals.
-In addition, this package provides worst-case analysis of the tightness for the confidence intervals, a feature that is not present in other software for binomial confidence intervals.
-Practically, this feature assists the user in understanding how many samples an experiment should have in order to meet a desired level of accuracy in inferring the value of $p$.
-
-
-# Comparison to Existing Software
-There are many existing software packages for computing binomial confidence intervals.
-binomial_cis differs from the existing software by providing:
-
-1. Open-source implementations for the optimal binomial confidence intervals given by [@eudey1949] and formalized in [@lehmann_textbook].
-
-2. Functionality for worst-case analysis of the tightness for the confidence intervals, which helps guide users on selecting the sample size for their experiments.
-
-
-# Research Usage
-
-binomial_cis has been used to compute confidence intervals for the success rate of robots in simulated and real-world tasks [@vincent2024generalizable].
-
-
-
-# Acknowledgements
-
-Financial support was provided by Toyota Research Institute.
-
-
-
-# References
-
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