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SuperGauss  

Superfast Likelihood Inference for Stationary Gaussian Time Series
View on CRAN: Click here


Download and install SuperGauss package within the R console
Install from CRAN:
install.packages("SuperGauss")

Install from Github:
library("remotes")
install_github("cran/SuperGauss")

Install by package version:
library("remotes")
install_version("SuperGauss", "2.0.3")



Attach the package and use:
library("SuperGauss")
Maintained by
Martin Lysy
[Scholar Profile | Author Map]
First Published: 2017-07-06
Latest Update: 2022-02-24
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.
How to cite:
Martin Lysy (2017). SuperGauss: Superfast Likelihood Inference for Stationary Gaussian Time Series. R package version 2.0.3, https://cran.r-project.org/web/packages/SuperGauss. Accessed 30 Apr. 2025.
Previous versions and publish date:
1.0.1 (2019-03-12 23:36), 1.0.2 (2020-02-27 13:40), 1.0 (2017-07-06 01:00), 2.0.0 (2020-09-21 20:20), 2.0.1 (2020-10-03 17:20), 2.0.2 (2021-10-19 19:30)
Other packages that cited SuperGauss R package
View SuperGauss citation profile
Other R packages that SuperGauss depends, imports, suggests or enhances
Complete documentation for SuperGauss
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