Metadata-Version: 2.1
Name: tealang
Version: 0.2
Summary: Tea: A High-level Language and Runtime System to Automate Statistical Analysis
Home-page: https://github.com/emjun/tea-lang
Author: Eunice Jun
Author-email: emjun@cs.washington.edu
License: Apache License 2.0
Description: # tea-lang [![Build Status](https://travis-ci.com/emjun/tea-lang.svg?branch=master)](https://travis-ci.com/emjun/tea-lang)
        
        # [WIP] Tea: A High-level Language and Runtime System for Automating Statistical Analyses
        
        ## What is Tea?
        Tea is a domain specific programming language that automates statistical test
        selection and execution. Tea is currently written in/for Python. 
        
        Tea has an <a href='https://arxiv.org/pdf/1904.05387.pdf'>academic Arxiv paper</a>. 
        
        Users provide 5 pieces of information: 
        * the *dataset* of interest, 
        * the *variables* in the dataset they want to analyze, 
        * the *study design* (e.g., independent, dependent variables),
        * the *assumptions* they make about the data based on domain knowledge(e.g.,
        a variable is normally distributed), and
        * a *hypothesis*.
        
        Tea then "compiles" these into logical constraints to select valid
        statistical tests. Tests are considered valid if and only if *all* the
        assumptions they make about the data (e.g., normal distribution, equal
        variance between groups, etc.) hold. Tea then finally executes the valid tests.
        
        ## What kinds of statistical analyses are possible with Tea?
        Tea currently provides a module to conduct Null Hypothesis Significance
        Testing (NHST). 
        
        *We are actively working on expanding the kinds of analyses Tea can support. Some ideas we have: Bayesian inference and linear modeling.*
        
        ## How can I use Tea?
        Tea will **very soon** be available on pip! Check back for updates :)
        
        ## How can I cite Tea?
        For now, please cite it!: 
        ```  
        article{JunEtAl2019:Tea,
          title={Tea: A High-level Language and Runtime System for Automating Statistical Analysis},
          author={Jun, Eunice and Daum, Maureen and Roesch, Jared and Chasins, Sarah E. and Berger, Emery D. and Just, Rene and Reinecke, Katharina},
          journal={Arxiv},
          year={2019}
        }
        ```
        
        ## How reliable is Tea?
        Tea is currently a research prototype. Our constraint solver is based on
        statistical texts (see <a href='https://arxiv.org/pdf/1904.05387.pdf'>our paper</a> for more info). 
        
        If you find any bugs, please let us know (email Eunice at emjun [at] cs.washington.edu)!
        
        ## I want to collaborate! Where do I begin?
        This is great! We're excited to have new collaborators. :) 
        
        To contribute *code*, please see <a href='./CONTRIBUTING.md'> docs and
        gudielines</a> and open an issue or pull request. 
        
        If you want to use Tea for a
        project, talk about Tea's design, or anything else, please get in touch: emjun at
        cs.washington.edu.
        
        ## Where can I learn more about Tea?
        Please find more information at <a href='https://www.tea-lang.org'>our website</a>. 
        
        ## I have ideas. I want to chat. 
        Please reach out! We are nice :): emjun at cs.washington.edu
        
        
        ### By the way, why Python?
        Python is a common language for data science. We hope Tea can easily integrate
        into user workflows. 
        
        *We are working on compiling Tea programs to different target languages, including R.*
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
