Implementation of a Metropolis-within-Gibbs MCMC algorithm to flexibly estimate the spectral density of a stationary time series. The algorithm updates a nonparametric B-spline prior using the Whittle likelihood to produce pseudo-posterior samples and is based on the work presented in Edwards, M.C., Meyer, R. and Christensen, N., Statistics and Computing (2018).
This package allows the user to flexibly estimate the spectral density of a stationary time series using a Bayesian nonparametric B-spline prior (of any degree). It works particularly well for complicated spectral structures (compared to the Bernstein polynomial prior).
The primary function gibbs_bspline is straightforward to use. Most of the arguments are defaults (i.e., a noninformative prior). All you need to do is input a numeric vector (your time series), the number of iterations to run the MCMC algorithm for, and the amount of burn-in.
Download from CRAN. Use install.packages("bsplinePsd") in R.