semvec/pypi-packages huggingface dataset:
scia 2.1.1
A Comprehensive most updated Python package for Single Case Design Analysis
Requires Python: >=3.9

# scia

scia is a robust Python package dedicated to single-case data analysis. Designed for researchers and practitioners working with single-case experimental designs.

**scia** provides a comprehensive suite of statistical methods to analyze, interpret, and report data effectively. The package includes functionality to create and preprocess single-case data frames, fill in missing values, filter data, and compute key non-overlap and effect size metrics. Advanced methods for evaluating treatment effects and trends are available through functions such as autocorr for autocorrelation analysis and bayesplm for Bayesian piecewise linear regression. Additional tools like pnd, pem, pet, nap, pand, ird, tau_u, and corrected_tau offer further analytical capabilities to support rigorous, data-driven decision making.

-Documentation is on going and reading, please be patient with it.

## 📚 Documentation

<div align="center">
  <a href="https://ahsankhodami.github.io/scia/intro.html">
    <img src="https://img.shields.io/badge/Documentation-Read%20Now-blue?style=for-the-badge&logo=read-the-docs" alt="Documentation">
  </a>
</div>

<br>

✨ **Key Features** ✨
- Comprehensive statistical methods for single-case analysis
- Missing value handling and data preprocessing
- Advanced treatment effect evaluation
- Bayesian piecewise linear regression
- Multiple non-overlap and effect size metrics
youtube-transcript-mcp 0.2.1
MCP server for fetching YouTube transcripts
Requires Python: >=3.10

# YouTube Transcript MCP Server

[![PyPI version](https://img.shields.io/pypi/v/youtube-transcript-mcp.svg)](https://pypi.org/project/youtube-transcript-mcp/)

A Model Context Protocol server that allows you to download subtitles from YouTube and connect them to a LLM. This is just a thin wrapper on the youtube-transcript-api package.

## Features

- Download transcripts of YouTube videos
- Optionally include timestamps
- Works with any MCP-compatible client

## Installation

### Option 1: Using uv (Recommended)

Install [uv](https://github.com/astral-sh/uv) is a fast Python package installer and resolver.

```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```

### Option 2: Using pip

If you prefer not to use uv, you can install using standard pip:

```bash
pip install youtube-transcript-mcp
```

## Usage

### In your MCP client configuration:

#### If using uv:

The `uvx` command is provided by uv and allows you to run Python packages directly:

```json
"mcpServers": {
    "youtube": {
      "command": "uvx",
      "args": ["youtube-transcript-mcp"]
    },
}
```

#### If using standard Python:

```json
"mcpServers": {
    "youtube": {
      "command": "python",
      "args": ["-m", "youtube_transcript_mcp"]
    },
}
```

## License

MIT