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bsplinePsd: Bayesian Nonparametric Spectral Density Estimation Using B-Spline Priors
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>.
Getting started
Browse package contents
Package details |
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| Author | Matthew C. Edwards [aut, cre], Renate Meyer [aut], Nelson Christensen [aut] |
| Maintainer | Matthew C. Edwards <matt.edwards@auckland.ac.nz> |
| License | GPL (>= 3) |
| Version | 0.6.0 |
| Package repository | View on CRAN |
| Installation |
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