semvec/pypi-packages huggingface dataset:
fastglmm 0.1.0
Fast Generalized Linear Mixed Models in Python
Requires Python: >=3.8

# fastglmm

**fastglmm** is a fast implementation of a Generalized Linear Mixed Model (GLMM) for count/binomial data with variance components.  
It is inspired by **PQLseq** (Sun et al. 2019; PMID: 30020412), with added flexibility and significant performance improvements.

## ✨ Features

- Supports **Binomial family with logit link**
- Order-of-magnitude faster than PQLseq
- Handles **variance components** (`tau1`, `tau2`) with options:
  - Fixed values
  - Inference from data
- Stable Newton-Raphson updates with adaptive step size
- Regularization for numerical stability
- Easy to use API similar to `statsmodels`

## 📦 Installation
You can easily install fastGLMM via Conda:
```bash
conda install -c conda-forge fastglmm
```

## 🚀 Usage
```python
import numpy as np
from fastglmm import GLMM

# Simulated data
n = 100
np.random.seed(0)
X = np.hstack((np.ones((n, 1)), np.random.randn(n, 2)))
Y = np.hstack((np.random.randint(0, 10, (n, 1)), np.random.randint(1, 10, (n, 1))))
G = np.random.randn(n, 500)
K = G @ G.T

# Fit model
res = GLMM(X, Y, K).fit()

# Summary
param, coef = res.summary()
print(param)
print(coef)
```
watercrawl-openai 0.1.1
WaterCrawl OpenAI Plugin
Requires Python: >=3.11

# Watercrawl-OpenAI Plugin

__version__: 0.0.1
__author__: Watercrawl

[![PyPI version](https://badge.fury.io/py/watercrawl-openai.svg)](https://badge.fury.io/py/watercrawl-openai)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)


## Description
This plugin allows you to use OpenAI's LLM to extract information from crawled content.


## Installation

You can install this plugin using pip:

```bash
pip install watercrawl-openai
```

## Usage
To use this plugin in your Watercrawl project, import it as follows:

In the WaterCrawl project environments add the following:
```python
WATER_CRAWL_PLUGINS="watercrawl_openai.OpenAIPlugin,...rest_of_plugins"
```

## Configuration

Make sure to set the following environment variables:

```env
OPENAI_API_KEY="Your OpenAI API key"
EXTRACT_SYSTEM_PROMPT="Your system prompt"
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License. See the LICENSE file for details.

## Links

GitHub: https://github.com/watercrawl/watercrawl-openai
linkloom 0.0.2
LinkLoom is a command-line bookmark manager that allows you to save and find your bookmarks using natural language queries. Instead of searching by tags or keywords, you can search by meaning.
Requires Python: >=3.9
Keywords: Bookmark, Organize, Productivity, Fast, Vector Search


LinkLoom is a command-line bookmark manager that allows you to save and find your bookmarks using natural language queries. Instead of searching by tags or keywords, you search by meaning.

Forget trying to remember the exact title of an article you saved three months ago. With LinkLoom, you can just ask:

```bash
lloom find "that article about black hole information paradox"
```

and get relevant results instantly.

## How It Works

LinkLoom operates by understanding the semantic meaning of your bookmarks, not just their text.

* **Storage:** Bookmarks (URL, notes) are stored in a local SQLite database.
* **Content Extraction:** The main text content of the webpage is extracted using [trafilatura](https://github.com/adbar/trafilatura).
* **Semantic Embedding:** The text and your note are converted into vector embeddings using a SentenceTransformer model.
* **Vector Storage:** Embeddings are stored in a local [ChromaDB](https://www.trychroma.com/) for efficient similarity search.
* **Semantic Search:** Your queries are also embedded, and LinkLoom returns the most semantically similar bookmarks.


## Installation

LinkLoom can be installed in two ways:

* From **PyPI** (recommended for most users)
* From **source** (recommended for contributors)

### Prerequisites

* Python **3.9+**
* `git` (only for source installation)

### 1. Create and Activate a Virtual Environment

It is strongly recommended to use a virtual environment.

```bash
python3 -m venv .venv
```

Activate it:

* On Linux/macOS:

  ```bash
  source .venv/bin/activate
  ```
* On Windows:

  ```
  .venv\Scripts\activate
  ```

---

### 2. Install from PyPI (Stable)

The easiest way is to install directly from PyPI:

```bash
pip install linkloom
```

---

### 3. Install from Source (Development)

If you want to contribute or run the latest changes:

```bash
git clone https://github.com/neirzhei/LinkLoom.git
cd LinkLoom
```

Install [uv](https://github.com/astral-sh/uv) (fast build tool):

```bash
pip install uv
```

Then install in editable mode:

```bash
uv pip install -e .
```



## Usage

Make sure your virtual environment is active (`source .venv/bin/activate`) before using the CLI.

### Adding a Bookmark

```bash
lloom add "https://neirzhei.github.io/article/art-in-weaponry.html" --note "might check out miyamoto musashi's book"
```

Output:

```bash
Saved: id=1
```

### Finding a Bookmark

```bash
lloom find "miyamoto's view on art"
```

Output:

```bash
https://neirzhei.github.io/article/art-in-weaponry.html
```
---