Description
Bayesian Nonparametric Spectral Density Estimation Using B-Spline Priors.
Description
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). <doi.org/10.1007/s11222-017-9796-9>.
README.md
Why should I use bsplinePsd?
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).
How do I use bsplinePsd?
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.
How do I get bsplinePsd?
Download from CRAN. Use install.packages("bsplinePsd") in R.