semvec/pypi-packages huggingface dataset:
mkdocs-render-stopper 0.1.1
A MkDocs plugin to stop rendering at a specific tag.
License: MIT
Requires Python: >=3.6

# mkdocs-render-stopper

A MkDocs plugin to stop rendering at a specific tag.

## Installation

Install the plugin using pip:

```bash
pip install mkdocs-render-stopper
```

## Usage

``` markdown
[STOP]
```

## License
The MIT License (MIT)

Copyright (c) 2023 Mikalai Lisitsa

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
aisdc 1.2.0
Tools for the statistical disclosure control of machine learning models
License: MIT
Requires Python: <3.12,>=3.9
Keywords: data-privacy, data-protection, machine-learning, privacy, privacy-tools, statistical-disclosure-control

[![License](https://img.shields.io/badge/license-MIT-blue.svg?style=flat)](https://opensource.org/licenses/MIT)
[![Latest Version](https://img.shields.io/github/v/release/AI-SDC/AI-SDC?style=flat)](https://github.com/AI-SDC/AI-SDC/releases)
[![DOI](https://zenodo.org/badge/518801511.svg)](https://zenodo.org/badge/latestdoi/518801511)
[![codecov](https://codecov.io/gh/AI-SDC/AI-SDC/branch/main/graph/badge.svg?token=AXX2XCXUNU)](https://codecov.io/gh/AI-SDC/AI-SDC)
[![Python versions](https://img.shields.io/pypi/pyversions/aisdc.svg)](https://pypi.org/project/aisdc)

# AI-SDC

A collection of tools and resources for managing the [statistical disclosure control](https://en.wikipedia.org/wiki/Statistical_disclosure_control) of trained [machine learning](https://en.wikipedia.org/wiki/Machine_learning) models. For a brief introduction, see [Smith et al. (2022)](https://doi.org/10.48550/arXiv.2212.01233).

The `aisdc` package provides:
* A variety of privacy attacks for assessing machine learning models.
* The safemodel package: a suite of open source wrappers for common machine learning frameworks, including [scikit-learn](https://scikit-learn.org) and [Keras](https://keras.io). It is designed for use by researchers in Trusted Research Environments (TREs) where disclosure control methods must be implemented. Safemodel aims to give researchers greater confidence that their models are more compliant with disclosure control.

## Installation

[![PyPI package](https://img.shields.io/pypi/v/aisdc.svg)](https://pypi.org/project/aisdc)

Install `aisdc` and manually copy the [`examples`](examples/).

To install only the base package, which includes the attacks used for assessing privacy:

```
$ pip install aisdc
```

To additionally install the safemodel package:

```
$ pip install aisdc[safemodel]
```

Note: macOS users may need to install libomp due to a dependency on XGBoost:
```
$ brew install libomp
```

## Running

See the [`examples`](examples/).

## Acknowledgement

This work was funded by UK Research and Innovation under Grant Numbers MC_PC_21033 and MC_PC_23006 as part of Phase 1 of the [DARE UK](https://dareuk.org.uk) (Data and Analytics Research Environments UK) programme, delivered in partnership with Health Data Research UK (HDR UK) and Administrative Data Research UK (ADR UK). The specific projects were Semi-Automatic checking of Research Outputs (SACRO; MC_PC_23006) and Guidelines and Resources for AI Model Access from TrusTEd Research environments (GRAIMATTER; MC_PC_21033).­This project has also been supported by MRC and EPSRC [grant number MR/S010351/1]: PICTURES.

<img src="docs/source/images/UK_Research_and_Innovation_logo.svg" width="20%" height="20%" padding=20/> <img src="docs/source/images/health-data-research-uk-hdr-uk-logo-vector.png" width="10%" height="10%" padding=20/> <img src="docs/source/images/logo_print.png" width="15%" height="15%" padding=20/>