The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

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:

Please use the canonical form https://CRAN.R-project.org/package=SuperGauss to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.