Metadata-Version: 2.4
Name: gpplus
Version: 0.1.0.0
Summary: Python library for Generalized Gaussian Process Modeling
Author-email: PMACS Lab <raminb@uci.edu>
License-Expression: MIT
Description-Content-Type: text/markdown
Requires-Dist: gpytorch>=1.14
Requires-Dist: linear_operator>=0.6
Requires-Dist: joblib>=1.4.2
Requires-Dist: matplotlib>=3.10.1
Requires-Dist: numpy>=2.2.4
Requires-Dist: torch>=2.5.1
Requires-Dist: tqdm>=4.67.1

# gpplus

GPPlus library

## Overview

GPPlus is a Python library that provides generalized Gaussian Process modeling. This repository contains the source code and documentation for the library.

## Installation

To install the package, follow these steps:

1. Clone the repository:

   git clone https://github.com/Bostanabad-Research-Group/gp-private.git

2. Navigate to the gp-private directory:

   cd gp-private

3. Install the package using pip:

   pip install .

## Usage

After installation, you can import and use the library in your Python scripts. For example:

import gpplus  # or the appropriate module name
''' Your code here using gpplus '''

## Contributing

We welcome contributions from the community! Please, check our [contributing guideline](CONTRIBUTING.md).

## More About GP+

GP+ is an open-source library for kernel-based learning via Gaussian processes (GPs). It systematically integrates nonlinear manifold learning techniques with GPs for single and multi-fidelity emulation, calibration of computer models, sensitivity analysis, and Bayesian optimization. GP+ is built on PyTorch and provides a user-friendly and object-oriented tool for probabilistic learning and inference. 

For more detailed information, refer to our paper: ["GP+: A Python Library for Kernel-based Learning via Gaussian Processes"](https://www.sciencedirect.com/science/article/pii/S0965997824000930?dgcid=author).

## Citing Us
If you use GP+ in your work, please use the following citation:
```
Yousefpour, Amin; Zanjani Foumani, Zahra; Shishehbor, Mehdi; Mora, Carlos; Bostanabad, Ramin. "GP+: A Python Library for Kernel-based Learning via Gaussian Processes." Advances in Engineering Software (2024). https://doi.org/10.1016/j.advengsoft.2024.103686.
```

## License

MIT License
