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 uwaterloo.ca>
License:	GPL-3
NeedsCompilation:	yes
SystemRequirements:	fftw3 (>= 3.1.2)
Materials:	NEWS
CRAN checks:	SuperGauss results

Documentation:

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

Downloads:

Package source:	SuperGauss_2.0.3.tar.gz
Windows binaries:	r-devel: SuperGauss_2.0.3.zip, r-release: SuperGauss_2.0.3.zip, r-oldrel: SuperGauss_2.0.3.zip
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

Linking:

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