update lock file

README.md

@@ -13,20 +13,27 @@
 ![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)
 [![types - Mypy](https://img.shields.io/badge/types-Mypy-blue.svg)](https://github.com/python/mypy)
 [![Website](https://img.shields.io/website?label=documentation&up_message=online&url=https%3A%2F%2Fguest400123064.github.io/ezgatr)](https://guest400123064.github.io/ezgatr/ezgatr.html)
+[![PyPI](https://img.shields.io/pypi/v/ezgatr)](https://pypi.org/project/ezgatr/)
 [![Python 3.10](https://img.shields.io/badge/python-%203.9%20|%203.10%20|%203.11-blue.svg)](https://www.python.org/downloads/release/python-3100/)
 
 ## What is EzGATr?
 **EzGATr** (Easy Geometric Algebra Transformer) intends to be a simple-to-use and lightweight Python library for building 3D [Geometric Algebra Transformers (GATr)](https://arxiv.org/abs/2305.18415). It is a collection of operators, modules, utilities, etc. build on top of [PyTorch](https://pytorch.org/). In addition, EzGATr also seeks to bridge the gap between the mathematical formulations and corresponding implementations through extensive documentation and explanations to facilitate learning and potential future optimizations.
 
 ## Installation
-EzGATr is currently in development and not yet available on PyPI. To install it, you can clone the repository and install it using `pip`. Use the `-e` flag to install it in editable mode for quick changes.
+EzGATr is currently under active development. To install it, you can clone the repository and install it using `pip`. Use the `-e` flag to install it in editable mode for making quick changes.
 
 ```bash
 $ git clone https://github.com/Guest400123064/ezgatr.git
 $ cd ezgatr
 $ pip install -e .
 ```
 
+Alternatively, one can directly install it from PyPI.
+
+```bash
+$ pip install ezgatr
+```
+
 ## Usage
 The quick start example shown below demonstrates how to use EzGATr to build a equivariant network with one input/output channel (e.g., a point cloud) and four hidden channels. The mock input data contains a batch of eight samples, each with 256 3D objects embedded as multi-vectors.