Jalal A. Nasiri ‡ j.nasiri@irandoc.ac.ir † Faculty of Electrical and Computer Engineering, Islamic Azad University, North Tehran Branch, Tehran, Iran ‡ Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran Abstract This paper presents LIBTwinSVM, a free, efficient, and open source library for Twin Support Vector Machines (TSVMs). Our library provides a set of useful functionalities such as fast TSVMs estimators, model selection, visualization, a graphical user interface (GUI) application, and a Python application programming interface (API). The benchmarks results indicate the effectiveness of the LIBTwinSVM library for large-scale classification problems. Keywords: TwinSVM, classification, open source, GUI, API 1. Introduction Twin Support Vector Machine (TSVM) is an extension of the Support Vector Machine (SVM), which was proposed by Jayadeva et al. (2007). TSVM does binary classification using two nonparallel hyperplanes. Each of which is as close as possible to the samples of its own class and far from the samples of the other class. The two nonparallel hyperplanes are obtained by solving two smaller-sized Quadratic Programming Problems (QPPs).