Package: SuperGauss 2.0.3

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.

Authors:Yun Ling [aut], Martin Lysy [aut, cre]

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26 exports 0.71 score 4 dependencies 2 dependents 31 scripts 366 downloads

Last updated 3 years agofrom:098cf8989f. Checks:OK: 9. Indexed: yes.

Exports:%*%acf2incracf2msdas.ToeplitzcholXZcholZXCirculantdeterminantdnormtzdSnormfbm_msdis.Toeplitzmatern_acfmsd2acfncolNormalCirculantNormalToeplitznrowpex_acfrnormtzrSnormSnorm.gradSnorm.hesssolvetoep.multToeplitz

Dependencies:fftwR6RcppRcppEigen