tag:crantastic.org,2005:/packages/SuperGaussLatest activity for SuperGauss2019-03-12T22:43:10Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/858882019-03-12T22:43:10Z2019-03-12T22:43:10ZSuperGauss was upgraded to version 1.0.1<a href="/packages/SuperGauss">SuperGauss</a> was <span class="action">upgraded</span> to version <a href="/packages/SuperGauss/versions/81670">1.0.1</a><br /><h3>Package description:</h3><p>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.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/642262017-07-05T23:22:07Z2017-07-05T23:22:07ZSuperGauss was released<a href="/packages/SuperGauss">SuperGauss</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>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.</p>crantastic.org