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.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.0 by Martin Lysy, 2 years ago

Browse source code at

Authors: Yun Ling [aut] , Martin Lysy [aut, cre]

Documentation:   PDF Manual  

GPL-3 license

Imports stats, methods, Rcpp, fftw

Suggests knitr, rmarkdown, testthat, mvtnorm, numDeriv

Linking to Rcpp, RcppEigen

System requirements: FFTW (>= 3.1.2)

See at CRAN