SuperGauss: Superfast Likelihood Inference for Stationary Gaussian TimeSeries
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: | 2.0.4 |
| Depends: | R (≥ 3.0.0) |
| Imports: | stats, methods,R6,Rcpp (≥ 0.12.7),fftw |
| LinkingTo: | Rcpp,RcppEigen |
| Suggests: | knitr,rmarkdown,testthat,mvtnorm,numDeriv |
| Published: | 2025-09-10 |
| DOI: | 10.32614/CRAN.package.SuperGauss |
| Author: | Yun Ling [aut], Martin Lysy [aut, cre] |
| Maintainer: | Martin Lysy <mlysy at uwaterloo.ca> |
| BugReports: | https://github.com/mlysy/SuperGauss/issues |
| License: | GPL-3 |
| URL: | https://github.com/mlysy/SuperGauss |
| NeedsCompilation: | yes |
| SystemRequirements: | fftw3 (>= 3.1.2) |
| Materials: | NEWS |
| CRAN checks: | SuperGauss results |
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