SuperGauss: Superfast Likelihood Inference for Stationary Gaussian Time Series
Version 1.0

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.

Package details

AuthorYun Ling [aut], Martin Lysy [aut, cre]
Date of publication2017-07-05 23:00:36 UTC
MaintainerMartin Lysy <[email protected]>
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the SuperGauss package in your browser

Any scripts or data that you put into this service are public.

SuperGauss documentation built on July 6, 2017, 1:06 a.m.