semvec/pypi-packages huggingface dataset:
PasteScript 3.7.0
A pluggable command-line frontend, including commands to setup package file layouts
License: MIT
Requires Python: >=3.8
Keywords: web wsgi setuptools framework command-line setup

PasteScript is a pluggable command-line tool.

**Note**: Paste Script is being maintained on life support. That
means that critical bugs will be fixed, and support for new versions
of Python will be handled, but other than that new features are not
being considered.

**With version 3.6.0 pastescript development moves to the pasteorg GitHub
organization and will be going deeper into maintenance mode unless
more active maintainers step forward to take over. "Deeper" in this
case means that releases will be much less frequent and patches
will only be accepted for security issues or major problems. Current
consumers of pastescript should prepare to migrate away to more modern
solutions.**

It includes some built-in features;

* Create file layouts for packages.  For instance, ``paster create
  --template=basic_package MyPackage`` will create a `setuptools
  <https://pythonhosted.org/setuptools/>`_-ready
  file layout.

* Serving up web applications, with configuration based on
  `paste.deploy <https://docs.pylonsproject.org/projects/pastedeploy>`_.

The latest version is available in a `GitHub repository
<https://github.com/pasteorg/pastescript/>`_.

For the latest changes see the `news file
<https://pastescript.readthedocs.io/en/latest/news.html>`_.
ForeTiS 0.0.7
state-of-the-art and easy-to-use time series forecasting
License: MIT
Requires Python: >=3.8

<div align="left"><img src="https://raw.githubusercontent.com/grimmlab/ForeTiS/master/docs/image/Logo_ForeTiS_Text.png" height="80"/></div>

# ForeTiS: A Forecasting Time Series framework

[![Python 3.8](https://img.shields.io/badge/Python-3.8-3776AB)](https://www.python.org/downloads/release/python-388/)

ForeTiS is a Python framework that enables the rigorous training, comparison and analysis of time series forecasting for a variety of different models. 
ForeTiS includes multiple state-of-the-art prediction models or machine learning methods, respectively. 
These range from classical models, such as regularized linear regression over ensemble learners, e.g. XGBoost, to deep learning-based architectures, such as Multilayer Perceptron (MLP). 
To enable automatic hyperparameter optimization, we leverage state-of-the-art and efficient Bayesian optimization techniques. 
In addition, our framework is designed to allow an easy and straightforward integration and benchmarking of further prediction models.

## Documentation
For more information, installation guides, tutorials and much more, see our documentation: https://foretis.readthedocs.io/

## Contributors
This pipeline is developed and maintained by members of the [Bioinformatics lab](https://bit.cs.tum.de) lead by [Prof. Dr. Dominik Grimm](https://bit.cs.tum.de/team/dominik-grimm/):
- [Josef Eiglsperger, M.Sc.](https://bit.cs.tum.de/team/josef-eiglsperger/)
- [Florian Haselbeck, M.Sc.](https://bit.cs.tum.de/team/florian-haselbeck/)

## Citation
When using ForeTiS, please cite our publication:

**ForeTiS: A comprehensive time series forecasting framework in Python.** <br />
Josef Eiglsperger*, Florian Haselbeck* and Dominik G. Grimm. <br />
*Machine Learning with Applications, 2023.* [doi: 10.1016/j.mlwa.2023.100467](https://doi.org/10.1016/j.mlwa.2023.100467) <br />
**These authors have contributed equally to this work and share first authorship.* <br />