# STATINF¶

This library aims at re-implementing standard statistical tools (such as OLS, logistic regression, Neural Network) and is built on top of numpy for handling data and theano for differential computing. The objective is to implement new methodologies from research projects on these models. The library also provides a data generator for linear and binary data.

The library is pip-installable and the source code is available on my Git. For any question or suggestion of improvement, please contact me.

# Installation¶

You can get STATINF from PyPI with:

```
pip install statinf
```

The library is supported on Windows, Linux and MacOS.

STATINF tries to use the least number of dependencies possible:

pandas: used to convert data frames into arrays.

numpy : main library for data handling and matrix operations.

scipy: probability density functions.

theano: tensor operations and back-propagation for Deep Learning models.

matplotlib: plots of training performances.

pycof: basic information printing.

# Indices and tables¶

# Release and FAQ¶

- Library updates
- 1.0.28 - September 27, 2020 - Time series module
`statinf.stats.timeseries()`

available - 1.0.27 - September 13, 2020 - New module
`statinf.data.ProcessData.Scaler()`

- 1.0.23 - May 17, 2020 - New model
`GLM()`

and improved features for`OLS()`

- 1.0.21 - Apr 26, 2020 - New module
`stats()`

- 1.0.19 - Apr 17, 2020 - Update for
`OLS()`

summary - 1.0.16 - Mar 22, 2020 - New module
`BinaryPerformance()`

- 1.0.12 - Mar 10, 2020 - New optimizers available
- 1.0.7 - Feb 1, 2020 - New model
`MLP()`

- 1.0.28 - September 27, 2020 - Time series module
- FAQ