Superfast Likelihood Inference for Stationary Gaussian Time
Series // 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.