Metadata-Version: 2.1
Name: phdu
Version: 1.5b9
Summary: Automatically store/load data in a tidy, efficient way. Includes functions for data visualization and analysis.
Home-page: https://github.com/medinajorge/PhD-utils
Download-URL: https://github.com/medinajorge/PhD-utils/archive/refs/tags/v1.5-beta.tar.gz
Author: Jorge Medina Hernández
Author-email: medinahdezjorge@gmail.com
Keywords: science,statistics,tidy,project organization,project,organization,path,storage
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: GNU Affero General Public License v3
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Office/Business
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3
Description-Content-Type: text/markdown
Provides-Extra: all
Provides-Extra: basic
Provides-Extra: matplotlib
Provides-Extra: plotly
Provides-Extra: colorlover
Provides-Extra: statsmodels
Provides-Extra: r
Provides-Extra: sklearn
License-File: LICENSE.md

# PhD-utils

For people that have to compute and store a large variety of data and/or perform statistical inference.

## Keep your files tidy!

Don't spend time creating directories, deciding filenames, saving, loading, etc. Decorators `savefig` & `savedata` will do it for you with optimal compression. More info at the `tidypath` [repository](https://github.com/medinajorge/tidypath).

## Estimate confidence intervals
The module `phdu.resample` allows calls to the `resample` [R package](https://cran.r-project.org/web/packages/resample/resample.pdf).
- Provides CI and permutation tests.
- CIs can account narrowness bias, skewness and other errors in CI estimation, as indicated in the [article](https://arxiv.org/abs/1411.5279)
- Alternatively, use `phdu.stats.bootstrap` for numba-accelerated computation (does not call `resample`).

## Bootstrap-based power analysis.
Calculate the power for accepting H0 and estimate the needed sample size.
Function `power_analysis` in `phdu.stats.bootstrap` follows Efron-Tshibirani: An introduction to the bootstrap,  p. 381-384.

## Numba-accelerated permutation tests
Module `phdu.stats.tests.permutation`. 
- Permutation tests for any statistic. 
- Includes paired and block cases.

## Demo
Please check the [example notebook](https://github.com/medinajorge/PhD-utils/blob/master/tests/Example.ipynb).

## Documentation
[Github pages](https://medinajorge.github.io/PhD-utils/phdu.html)

## Install
- For the R compatible installation first install R:

  ```conda install -c conda-forge r r-essentials r-base```
  
- Install with dependencies:

  ```pip install phdu[dependencies]```
  
  Where `dependencies` can be `base` (recommended), `all`, `r` (needed for `resample` to work), `statsmodels`, `matplotlib` or `plotly`.
