nextrngbook 0.1.0.post1

A Python Random Number Generation Package for RNG Book

NextRNGBook: A Python Random Number Generation Package for RNG Book

Introduction

The goal of NextRNGBook package is to incorporate a variety of high-quality random number generators (RNGs) from Random Number Generators for Computer Simulation and Cyber Security [1]. Designed for seamless compatibility with NumPy, this Python package can integrate easily into existing workflows, offering a wide range of selections from state-of-the-art random number generation techniques suitable for scientific computing, large-scale simulations, and cryptographic applications.

The goal of designing high-quality random number generators is to produce variates that behave like truly random numbers. This means the generated variates can cover the space evenly over high dimensions, and do not repeat for a very long time. They can be generated efficiently across different systems, and they can pass a wide range of statistical tests that detect hidden patterns. A good RNG should perform reliably for large-scale simulations with a strong support for parallel computing, and an easy integration across various computing platforms. For security applications, generated variates need to be unpredictable, so that future values cannot be inferred from past outputs.

There are several high-quality RNGs to be implemented in this NextRNGBook Package which should provide a solid foundation for better statistical simulation and/or secure applications. Combining strong theoretical supports and great practical performance, NextRNGBook can help users to explore, evaluate, and apply high-quality RNGs in a modern Python environment.

Documentation & Distribution

For full details, please refer to the links.

• Documentation: NextRNGBook Documentation
• PyPI: NextRNGBook on PyPI

References

[1] Deng, L.-Y., Kumar, N., Lu, H. H.-S., & Yang, C.-C. (2025). Random Number Generators for Computer Simulation and Cyber Security: Design, Search, Theory, and Application (1st ed.). Springer. https://doi.org/10.1007/978-3-031-76722-7