摘要：Graphical abstractDisplay OmittedAbstractWe provide more technical details about the HLIBCov package, which is using parallel hierarchical (H-) matrices to:•Approximate large dense inhomogeneous covariance matrices with a log-linear computational cost and storage requirement.•Compute matrix-vector product, Cholesky factorization and inverse with a log-linear complexity.•Identify unknown parameters of the covariance function (variance, smoothness, and covariance length).These unknown parameters are estimated by maximizing the joint Gaussian log-likelihood function. To demonstrate the numerical performance, we identify three unknown parameters in an example with 2,000,000 locations on a PC-desktop.
