Error-correcting output codes is a technique for using binary classification models on multi-class classification prediction tasks. Error-Correcting Output Codes (ECOC) represents a successful framework to deal with these kinds of problems. Recent works in the ECOC framework appear notable performance improvements. The ECOC framework is a high-level tool to deal with multi-class categorization problems. As the error correcting output codes have error correcting ability and improve the generalization ability to base classification. This library contains both modern coding (one-versus-one, one-versus- all, dense-random, sparserandom, DECOC, forest- ECOC, and ECOC-ONE) and decoding designs (hamming, Euclidean, inverse hamming, laplacian, β-density, density, attenuated, lossbased, probabilistic kernel-based, and loss weighted) with the framework defined by the authors, as well as the option to include your own coding, decoding, and base classifier.