Vstats: A dependency-free Linear Algebra, Statistics, ML library written in V (github.com)

A new library called Vstats has been announced, offering a dependency-free solution for linear algebra, statistics, and machine learning, crafted entirely in the V programming language. This library enables users to perform various statistical computations, including mean, variance, and advanced metrics like ANOVA and confidence intervals, using generic types, which allows seamless compatibility with integers and floating-point numbers. Additionally, Vstats provides tools for building confusion matrices and calculating performance metrics for machine learning models, all while ensuring mathematical precision. The significance of Vstats lies in its potential to simplify the development process for machine learning practitioners and data scientists by offering a comprehensive, adaptable toolkit without external dependencies. This self-contained approach can enhance the accessibility of machine learning and statistical modeling, especially for those leveraging V. With completed modules addressing key areas such as linear algebra, optimization, and model evaluation, Vstats is poised to facilitate rapid prototyping and educational applications, making advanced statistical techniques more approachable for the AI/ML community.