SuperGauss: 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.

Version: 2.0.3
Depends: R (≥ 3.0.0)
Imports: stats, methods, R6, Rcpp (≥ 0.12.7), fftw
LinkingTo: Rcpp, RcppEigen
Suggests: knitr, rmarkdown, testthat, mvtnorm, numDeriv
Published: 2022-02-24
DOI: 10.32614/CRAN.package.SuperGauss
Author: Yun Ling [aut], Martin Lysy [aut, cre]
Maintainer: Martin Lysy <mlysy at>
License: GPL-3
NeedsCompilation: yes
SystemRequirements: fftw3 (>= 3.1.2)
Materials: NEWS
CRAN checks: SuperGauss results


Reference manual: SuperGauss.pdf
Vignettes: Superfast Likelihood Inference for Stationary Gaussian Time Series


Package source: SuperGauss_2.0.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): SuperGauss_2.0.3.tgz, r-oldrel (arm64): SuperGauss_2.0.3.tgz, r-release (x86_64): SuperGauss_2.0.3.tgz, r-oldrel (x86_64): SuperGauss_2.0.3.tgz
Old sources: SuperGauss archive

Reverse dependencies:

Reverse imports: AIUQ, LMN


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