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.

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- Package
- SuperGauss
- Type
- Package
- Title
- Superfast Likelihood Inference for Stationary Gaussian Time Series
- Version
- 1.0.1
- Date
- 2019-03-12
- 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, Rcpp (>= 0.12.7), fftw
- LinkingTo
- Rcpp, RcppEigen
- Suggests
- knitr, rmarkdown, testthat, mvtnorm, numDeriv
- VignetteBuilder
- knitr
- RoxygenNote
- 6.1.1
- Encoding
- UTF-8
- SystemRequirements
- fftw3 (>= 3.1.2)
- NeedsCompilation
- yes
- Packaged
- 2019-03-12 18:18:10 UTC; mlysy
- Author
- Yun Ling [aut], Martin Lysy [aut, cre]
- Maintainer
- Martin Lysy
- Repository
- CRAN
- Date/Publication
- 2019-03-12 22:36:08 UTC