Metadata-Version: 2.3
Name: earlysign
Version: 0.2.0
Summary: Early signs, faster decisions. A Python library for sequential/safe testing (alpha-spending, e-processes, etc.).
License: Apache-2.0
Author: Takeshi Teshima
Author-email: takeshi.78.teshima@gmail.com
Requires-Python: >=3.11,<4.0
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Provides-Extra: examples
Requires-Dist: matplotlib (>=3.10.6,<4.0.0) ; extra == "examples"
Requires-Dist: numpy (>=2.3.2,<3.0.0) ; extra == "examples"
Requires-Dist: scipy (>=1.16.1,<2.0.0)
Project-URL: Homepage, https://early-sign.github.io/EarlySign
Project-URL: Repository, https://github.com/early-sign/EarlySign
Description-Content-Type: text/markdown

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# EarlySign

<center>
<img src="https://raw.githubusercontent.com/early-sign/EarlySign/refs/heads/main/docs/logo.png" width="80%"><br/>
Early signs, faster decisions.
</center>

---

## What is this?

EarlySign is a Python library for sequential/safe testing (alpha-spending, e-processes, etc.).

1. Group sequential tests for interim analysis
    - By using alpha-spending functions to control the overall Type I error rate, you can stop early for efficacy or futility, making your experiments more efficient without compromising statistical integrity. This approach allows for a pre-specified number of interim analyses during an experiment.
1. e-processes for anytime-valid inference
    - It allows you to continuously monitor your experiments and make decisions as soon as the evidence is strong enough, without waiting for a predetermined sample size. This can lead to faster conclusions, saving time and resources, while maintaining statistical rigor.


## Quick Start

```
pip install earlysign
```

Please check our [up-to-date documentation](https://early-sign.github.io/EarlySign/) site for explanations, references, how-to's, and tutorials.

## Usage

This library supports the following steps in your experimentation.

1. Planning / Designing
1. Executing / Analyzing
1. Reporting / Visualizing
1. (optionally) Educating

