MOGPTK: The multi-output Gaussian process toolkit

We present MOGPTK, a Python package for multi-channel data modelling using Gaussian processes (GP). The aim of this toolkit is to make multi-output GP (MOGP) models accessible researchers, scientists, and practitioners alike. MOGPTK uses front-end relies on the PyTorch suite, thus enabling GPU-accelerated training. facilitates implementing entire pipeline modelling, including loading, parameter initialization, model learning, interpretation, up imputation extrapolation. implements main covariance kernels from literature, as well spectral-based initialization strategies. source code, tutorials examples in form Jupyter notebooks, together with API documentation, can be found GitHub repository: https://github.com/GAMES-UChile/mogptk.