SuperGauss: Superfast Likelihood Inference for Stationary Gaussian Time
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.
||R (≥ 3.0.0)
||stats, methods, R6, Rcpp (≥ 0.12.7), fftw
||knitr, rmarkdown, testthat, mvtnorm, numDeriv
||Yun Ling [aut],
Martin Lysy [aut, cre]
||Martin Lysy <mlysy at uwaterloo.ca>
||fftw3 (>= 3.1.2)
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