NEWS.md

In bsplinePsd: Bayesian Nonparametric Spectral Density Estimation Using B-Spline Priors

bsplinePsd 0.6.0

Have included a scaling/rescaling factor within the code to remove numerical errors when data is close to machine precision.

Have optimised the starting values for the Dirichlet process parameters.

bsplinePsd 0.5.0

Previous versions only allowed the user to use cubic B-spline densities. This version allows the user to choose any degree. Normalising the B-splines now uses a trivial integral formula.

The function gibbs_bspline can now handle odd length time series.

An S3 plot method has been included so the user can easily plot their PSD estimate.

bsplinePsd 0.2.0

Added an argument called k1 in the gibbs_bspline function. This allows the user to specify the starting value for parameter k. If well-chosen, this can speed up convergence significantly. The default is set to 20, which works well on all of the cases I have come across. If missing (NA), then a random integer between degree + 2 and kmax will be selected as the starting value for k.

bsplinePsd documentation built on May 2, 2019, 5:56 a.m.