Update paper

paper/automl-template.tex

@@ -107,7 +107,7 @@ \section*{Submission Checklist}
     %
   \item Did you discuss any potential negative societal impacts of your work?
     %
-    \answerYes{}
+    \answerNA{I can't think of any particular negative societal impacts of a Bayesian workflow template}
     %
   \item Did you read the ethics review guidelines and ensure that your paper
     conforms to them? \url{https://2022.automl.cc/ethics-accessibility/}
@@ -170,12 +170,12 @@ \section*{Submission Checklist}
     \texttt{README} with installation, and execution commands (either in the
     supplemental material or as a \textsc{url})?
     %
-    \answerYes{}
+    \answerYes{See \url{https://bibat.readthedocs.io/en/latest/_static/report.html} for instructions to reproduce the main example.}
     %
   \item Did you include a minimal example to replicate results on a small subset
     of the experiments or on toy data?
     %
-    \answerYes{}
+    \answerYes{See section "Generating Stan inputs" here \url{https://bibat.readthedocs.io/en/latest/_static/report.html\#preparing-the-data}}
     %
   \item Did you ensure sufficient code quality and documentation so that someone else
     can execute and understand your code?
@@ -185,12 +185,12 @@ \section*{Submission Checklist}
   \item Did you include the raw results of running your experiments with the given
     code, data, and instructions?
     %
-    \answerNA{}
+    \answerNo{This is unnecessary as the results are easy to reproduce and the results are large files that would be awkward to store online}
     %
   \item Did you include the code, additional data, and instructions needed to generate
     the figures and tables in your paper based on the raw results?
     %
-    \answerYes{}
+    \answerNo{To reproduce the figures from raw data run "make analysis" from the folder \texttt{example\_projects/baseball}, as described in the instructions here \url{https://bibat.readthedocs.io/en/latest/\_static/report.html}}
     %
   \end{enumerate}
   %
@@ -224,7 +224,7 @@ \section*{Submission Checklist}
     \item Did you include the new assets either in the supplemental material or as
     a \textsc{url} (to, e.g., GitHub or Hugging Face)?
     %
-    \answerYes{}
+    \answerYes{\url{https://github.com/teddygroves/bibat/}}
     %
   \end{enumerate}
   %

paper/bibliography.bib

@@ -249,14 +249,15 @@ @book{boxBayesianInferenceStatistical1992
   langid = {english},
   file = {/Users/tedgro/Zotero/storage/7GGTD6UY/Box and Tiao - 1992 - Bayesian inference in statistical analysis.pdf}
 }
-
-@article{strumbeljPresentFutureSoftware,
-  title = {Past, {{Present}}, and {{Future}} of {{Software}} for {{Bayesian Inference}}},
-  author = {{\v S}trumbelj, Erik and {Bouchard-C{\^o}t{\'e}}, Alexandre and Corander, Jukka and Gelman, Andrew and Rue, H{\aa}vard and Murray, Lawrence and Pesonen, Henri and Plummer, Martyn and Vehtari, Aki},
-  abstract = {Software tools for Bayesian inference have undergone rapid evolution in the past three decades, following popularisation of the first generation MCMC-sampler implementations. More recently, exponential growth in the number of users has been stimulated both by the active development of new packages by the machine learning community and popularity of specialist software for particular applications. This review aims to summarize the most popular software and provide a useful map for a reader to navigate the world of Bayesian computation. We anticipate a vigorous continued development of algorithms and corresponding software in multiple research fields, such as probabilistic programming, likelihood-free inference, and Bayesian neural networks, which will further broaden the possibilities for employing the Bayesian paradigm in exciting applications. Key words and phrases: statistics, data analysis, MCMC, computation, probabilistic programming.},
-  langid = {english},
-  file = {/Users/tedgro/Zotero/storage/F55H2HPD/Štrumbelj et al. - Past, Present, and Future of Software for Bayesian.pdf},
-  year = {2024}
+@article{vstrumbelj2024past,
+  title={Past, Present and Future of Software for Bayesian Inference},
+  author={Strumbelj, Erik and Bouchard-C{\^o}t{\'e}, Alexandre and Corander, Jukka and Gelman, Andrew and Rue, H{\aa}vard and Murray, Lawrence and Pesonen, Henri and Plummer, Martyn and Vehtari, Aki},
+  journal={Statistical Science},
+  volume={39},
+  number={1},
+  pages={46--61},
+  year={2024},
+  publisher={Institute of Mathematical Statistics}
 }
 
 @misc{grovesteddySphincter2024,
@@ -304,3 +305,14 @@ @misc{copierdevelopersCopier2024
   howpublished = {copier-org},
   keywords = {cookiecutter,copier-template,hacktoberfest,project-template,python,scaffolding}
 }
+
+@misc{wardWardBrianCookiecuttercmdstanpywrapper2024,
+  title = {{{WardBrian}}/Cookiecutter-Cmdstanpy-Wrapper},
+  author = {Ward, Brian},
+  year = {2024},
+  month = jan,
+  url = {https://github.com/WardBrian/cookiecutter-cmdstanpy-wrapper},
+  urldate = {2024-02-26},
+  abstract = {Easily wrap a Stan model in a Python package},
+  keywords = {cookiecutter,cookiecutter-python,cookiecutter-template,python,stan}
+}