PyDiM: a new Python library for Diffusion Model Analysis

Innovation diffusion analysis is an important tool for understanding how new ideas, products, and technologies spread through time. While there are existing packages in R for conducting this kind of analysis, there is a growing demand for similar tools in other data science-oriented programming languages, particularly Python. Python is a popular language for data analysis and machine learning, with a large and active community of users and developers. Having an innovation diffusion analysis library in Python would allow researchers and practitioners to leverage the language's strengths in data processing, visualization, and modeling. It would also provide a more accessible and user-friendly option for those who are more comfortable with Python than with R. Overall, this new Python library for innovation diffusion analysis fills a gap in the current landscape of data analysis tools, providing a valuable option for those who prefer Python and opening up new opportunities for research and innovation in this important area. This new Python library offers a comprehensive set of tools for conducting innovation diffusion analysis, including data preparation, visualization, and modeling. It is designed to be easy to use, with clear and intuitive functions and documentation. Additionally, it offers flexibility and customization options to meet the needs of a wide range of user