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 by Edwards, Meyer, and Christensen (2017) <arXiv:1707.04878>.

Version: 0.1.0
Imports: Rcpp (≥ 0.12.5), splines (≥ 3.2.3)
LinkingTo: Rcpp
Published: 2017-07-18
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
CRAN checks: bsplinePsd results

Downloads:

Reference manual: bsplinePsd.pdf
Package source: bsplinePsd_0.1.0.tar.gz
Windows binaries: r-devel: bsplinePsd_0.1.0.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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=bsplinePsd to link to this page.