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:
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Reverse dependencies:
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