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