Superfast Likelihood Inference for Stationary Gaussian Time Series R package SuperGauss version 2.0.4 Likelihood evaluations for stationary Gaussian time series are typically obtained via the Durbin-Levinson algorithm, which scales as O(n^2) in the number of time series observations. This package provides a "superfast" O(n log^2 n) algorithm written in C++, crossing over with Durbin-Levinson around n = 300. Efficient implementations of the score and Hessian functions are also provided, leading to superfast versions of inference algorithms such as Newton-Raphson and Hamiltonian Monte Carlo. The C++ code provides a Toeplitz matrix class packaged as a header-only library, to simplify low-level usage in other packages and outside of R. Software Depends: R (>= 3.0.0) Imports: stats, methods, R6, Rcpp (>= 0.12.7), fftw LinkingTo: Rcpp, RcppEigen Suggests: knitr, rmarkdown, testthat, mvtnorm, numDeriv Martin Lysy <mlysy@uwaterloo.ca> Comprehensive R Archive Network (CRAN) Yun Ling [aut], Martin Lysy [aut, cre] GPL-3 2025-09-10 application/tgz https://CRAN.R-project.org/package=SuperGauss doi:10.32614/CRAN.package.SuperGauss