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"author": "Eunice Jun",
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"description": "# tea-lang [](https://travis-ci.com/emjun/tea-lang)\n\n# [WIP] Tea: A High-level Language and Runtime System for Automating Statistical Analyses\n\n## What is Tea?\nTea is a domain specific programming language that automates statistical test\nselection and execution. Tea is currently written in/for Python. \n\nTea has an academic Arxiv paper. \n\nUsers provide 5 pieces of information: \n* the *dataset* of interest, \n* the *variables* in the dataset they want to analyze, \n* the *study design* (e.g., independent, dependent variables),\n* the *assumptions* they make about the data based on domain knowledge(e.g.,\na variable is normally distributed), and\n* a *hypothesis*.\n\nTea then \"compiles\" these into logical constraints to select valid\nstatistical tests. Tests are considered valid if and only if *all* the\nassumptions they make about the data (e.g., normal distribution, equal\nvariance between groups, etc.) hold. Tea then finally executes the valid tests.\n\n## What kinds of statistical analyses are possible with Tea?\nTea currently provides a module to conduct Null Hypothesis Significance\nTesting (NHST). \n\n*We are actively working on expanding the kinds of analyses Tea can support. Some ideas we have: Bayesian inference and linear modeling.*\n\n## How can I use Tea?\nTea will **very soon** be available on pip! Check back for updates :)\n\n## How can I cite Tea?\nFor now, please cite it!: \n``` \narticle{JunEtAl2019:Tea,\n title={Tea: A High-level Language and Runtime System for Automating Statistical Analysis},\n author={Jun, Eunice and Daum, Maureen and Roesch, Jared and Chasins, Sarah E. and Berger, Emery D. and Just, Rene and Reinecke, Katharina},\n journal={Arxiv},\n year={2019}\n}\n```\n\n## How reliable is Tea?\nTea is currently a research prototype. Our constraint solver is based on\nstatistical texts (see our paper for more info). \n\nIf you find any bugs, please let us know (email Eunice at emjun [at] cs.washington.edu)!\n\n## I want to collaborate! Where do I begin?\nThis is great! We're excited to have new collaborators. :) \n\nTo contribute *code*, please see docs and\ngudielines and open an issue or pull request. \n\nIf you want to use Tea for a\nproject, talk about Tea's design, or anything else, please get in touch: emjun at\ncs.washington.edu.\n\n## Where can I learn more about Tea?\nPlease find more information at our website. \n\n## I have ideas. I want to chat. \nPlease reach out! We are nice :): emjun at cs.washington.edu\n\n\n### By the way, why Python?\nPython is a common language for data science. We hope Tea can easily integrate\ninto user workflows. \n\n*We are working on compiling Tea programs to different target languages, including R.*\n\n",
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