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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.0.4 |
rolling linux/jammy R-4.5 | SuperGauss_2.0.4.tar.gz |
578.7 KiB |
2.0.4 |
rolling linux/noble R-4.5 | SuperGauss_2.0.4.tar.gz |
584.7 KiB |
2.0.4 |
rolling source/ R- | SuperGauss_2.0.4.tar.gz |
299.9 KiB |
2.0.4 |
latest linux/jammy R-4.5 | SuperGauss_2.0.4.tar.gz |
578.7 KiB |
2.0.4 |
latest linux/noble R-4.5 | SuperGauss_2.0.4.tar.gz |
584.7 KiB |
2.0.4 |
latest source/ R- | SuperGauss_2.0.4.tar.gz |
299.9 KiB |
2.0.4 |
2026-04-26 source/ R- | SuperGauss_2.0.4.tar.gz |
299.9 KiB |
2.0.4 |
2026-04-23 source/ R- | SuperGauss_2.0.4.tar.gz |
299.9 KiB |
2.0.4 |
2026-04-09 windows/windows R-4.5 | SuperGauss_2.0.4.zip |
1.3 MiB |
2.0.3 |
2025-04-20 source/ R- | SuperGauss_2.0.3.tar.gz |
311.0 KiB |