Package: SuperGauss
Type: Package
Title: Superfast Likelihood Inference for Stationary Gaussian Time
        Series
Version: 2.0.3
Date: 2022-02-24
Authors@R: c(person("Yun", "Ling", role = "aut"),
             person("Martin", "Lysy",
	            email = "mlysy@uwaterloo.ca",
		    role = c("aut", "cre")))
Description: 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.
License: GPL-3
Depends: R (>= 3.0.0)
Imports: stats, methods, R6, Rcpp (>= 0.12.7), fftw
LinkingTo: Rcpp, RcppEigen
Suggests: knitr, rmarkdown, testthat, mvtnorm, numDeriv
VignetteBuilder: knitr
RoxygenNote: 7.1.2
Encoding: UTF-8
SystemRequirements: fftw3 (>= 3.1.2)
NeedsCompilation: yes
Packaged: 2022-02-24 19:11:11 UTC; mlysy
Author: Yun Ling [aut],
  Martin Lysy [aut, cre]
Maintainer: Martin Lysy <mlysy@uwaterloo.ca>
Repository: CRAN
Date/Publication: 2022-02-24 20:40:02 UTC
Built: R 4.5.0; x86_64-apple-darwin20; 2025-01-26 14:33:14 UTC; unix
Archs: SuperGauss.so.dSYM
