semvec/pypi-packages huggingface dataset:
survive 0.3
Survival analysis in Python

=======
Survive
=======

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Survival Analysis in Python
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.. image:: https://img.shields.io/pypi/pyversions/survive.svg
    :target: https://pypi.org/project/survive/
    :alt: Python Version

.. image:: https://img.shields.io/pypi/v/survive.svg
    :target: https://pypi.org/project/survive/
    :alt: PyPI Package Version

.. image:: https://readthedocs.org/projects/survive-python/badge/?version=latest
    :target: https://survive-python.readthedocs.io/?badge=latest
    :alt: Documentation Status

.. image:: https://img.shields.io/github/license/artemmavrin/survive.svg
    :target: https://github.com/artemmavrin/survive/blob/master/LICENSE
    :alt: GitHub License

Survive is a Python 3 package built on top of `NumPy <http://www.numpy.org>`__
and `pandas <https://pandas.pydata.org>`__ that provides statistical tools for
the analysis of survival, lifetime, and event data.

Website: https://survive-python.readthedocs.io


Installation
~~~~~~~~~~~~

The latest version of Survive can be installed directly after cloning from
`GitHub <https://github.com/artemmavrin/survive>`__.

.. code-block :: shell

  git clone https://github.com/artemmavrin/survive.git
  cd survive
  make install

Moreover, Survive is on the
`Python Package Index (PyPI) <https://pypi.org/project/survive/>`__, so a recent
version of it can be installed with the `pip <https://pip.pypa.io/en/stable/>`__
tool.

.. code-block :: shell

  python -m pip install survive


Dependencies
~~~~~~~~~~~~

Survive relies on the following scientific computing packages.

* `NumPy <http://www.numpy.org>`__
* `pandas <https://pandas.pydata.org>`__
* `SciPy <https://www.scipy.org>`__
* `Matplotlib <https://matplotlib.org>`__


Case Studies
~~~~~~~~~~~~

`Leukemia remission times <https://survive-python.readthedocs.io/examples/Leukemia_Remission_Time_Dataset.html>`__
    A small dataset (42 total observations) separated into two groups with heavy
    right-censoring in one and none in the other.

`Channing House data <https://survive-python.readthedocs.io/examples/Channing_House_Dataset.html>`__
    A slightly larger dataset (462 total observations) separated into two groups
    with right-censoring and left truncation in both.


API Reference
~~~~~~~~~~~~~

The complete API Reference for Survive is available on
`Read the Docs <https://survive-python.readthedocs.io/api.html>`__.
randhy 2.0.2
Approximate randomisation library

# randhy
_Hypothesis thesting with approximate randomisation_


[![CircleCI](https://circleci.com/gh/savkov/randhy.svg?style=svg&circle-token=ff0102ad2d043f5548279e8f48a5fcde2297978b)](https://circleci.com/gh/savkov/randhy)
[![Maintainability](https://api.codeclimate.com/v1/badges/c21544109986302622de/maintainability)](https://codeclimate.com/github/savkov/randhy/maintainability)
[![Test Coverage](https://api.codeclimate.com/v1/badges/c21544109986302622de/test_coverage)](https://codeclimate.com/github/savkov/randhy/test_coverage)

Approximate randomisation is a significance testing approach suitable for NLP
problems.

### �� Why not a traditional t-test?

While randomisation tests are just as good as analytical approaches such as the 
_t-test_, they are better when the assumptions of the latter are not met and
they are also quite simple to implement.

### ��️ Installation

```bash
pip install randhy
```

### References

1. _[William Morgan, Statistical Hypothesis Tests for NLP - Stanford Computer Science (slides)](https://cs.stanford.edu/people/wmorgan/sigtest.pdf)_
2. _[Wassily Hoeffding. 1952. The Large-Sample Power of Tests Based on Permutations of Observations. Annals ofMathematical Statistics, 23, 169–192.](https://www.jstor.org/stable/2958014?seq=1#page_scan_tab_contents)_
3. _[Eric W. Noreen. 1989. Computer Intensive Methods forTesting Hypothesis. John Wiley & Sons](https://www.amazon.co.uk/Computer-Intensive-Methods-Testing-Hypotheses-Introduction/dp/0471611360)_
fnr 0.1.2
A command line utility to find & replace text.

FnR
=====

FnR is a command line utility powered by Python to substitute text in files.

This tool was developed to automate the process of opening a text editor, hitting ctrl+f, typing your search term, switching to replace, typing your replacement term, and repeating for multiple search terms. Instead, with FnR, you input all search and replacement terms at once and BAM!

FnR also supports repeatability by allowing the user to provide the mapping of search/replace terms in a file so if you find yourself repetitively replacing items in similar template files, FnR is perfect for automating your life. :)

## FnR?
The name *fnr* is a twisted acronym for _*Find & Replace*_, or F&R.