SuperGauss (1.0)

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.

Maintainer: Martin Lysy
Author(s): Yun Ling [aut], Martin Lysy [aut, cre]

License: GPL-3

Uses: fftw, Rcpp, mvtnorm, numDeriv, testthat, knitr, rmarkdown

Released over 2 years ago.