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>.
| Version: | 0.2.0 |
| Imports: | Rcpp (≥ 0.12.5), splines (≥ 3.2.3) |
| LinkingTo: | Rcpp |
| Published: | 2018-02-23 |
| Author: | Matthew C. Edwards [aut, cre], Renate Meyer [aut], Nelson Christensen [aut] |
| Maintainer: | Matthew C. Edwards <matt.edwards at auckland.ac.nz> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | yes |
| Materials: | NEWS |
| CRAN checks: | bsplinePsd results |
| Reference manual: | bsplinePsd.pdf |
| Package source: | bsplinePsd_0.2.0.tar.gz |
| Windows binaries: | r-devel: bsplinePsd_0.2.0.zip, r-release: bsplinePsd_0.1.0.zip, r-oldrel: bsplinePsd_0.1.0.zip |
| OS X El Capitan binaries: | r-release: bsplinePsd_0.1.0.tgz |
| OS X Mavericks binaries: | r-oldrel: bsplinePsd_0.1.0.tgz |
| Old sources: | bsplinePsd archive |
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