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.


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install.packages("SuperGauss")

1.0.1 by Martin Lysy, 8 months ago


Browse source code at https://github.com/cran/SuperGauss


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: fftw3 (>= 3.1.2)


See at CRAN