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: 1.0
Depends: R (≥ 3.0.0)
Imports: stats, methods, Rcpp (≥ 0.12.7), fftw
LinkingTo: Rcpp, RcppEigen
Suggests: knitr, rmarkdown, testthat, mvtnorm, numDeriv
Published: 2017-07-05
Author: Yun Ling [aut], Martin Lysy [aut, cre]
Maintainer: Martin Lysy <mlysy at uwaterloo.ca>
License: GPL-3
NeedsCompilation: yes
SystemRequirements: FFTW (>= 3.1.2)
CRAN checks: SuperGauss results

Downloads:

Reference manual: SuperGauss.pdf
Vignettes: SuperGauss
Package source: SuperGauss_1.0.tar.gz
Windows binaries: r-devel: SuperGauss_1.0.zip, r-release: SuperGauss_1.0.zip, r-oldrel: SuperGauss_1.0.zip
OS X binaries: r-release: SuperGauss_1.0.tgz, r-oldrel: SuperGauss_1.0.tgz

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