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>.

Version:	0.6.0
Imports:	Rcpp (≥ 0.12.5), splines (≥ 3.2.3)
LinkingTo:	Rcpp
Published:	2018-10-18
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
Materials:	README NEWS
CRAN checks:	bsplinePsd results

Documentation:

Reference manual:

bsplinePsd.pdf

Downloads:

Package source:	bsplinePsd_0.6.0.tar.gz
Windows binaries:	r-devel: bsplinePsd_0.6.0.zip, r-release: bsplinePsd_0.6.0.zip, r-oldrel: bsplinePsd_0.6.0.zip
macOS binaries:	r-release (arm64): bsplinePsd_0.6.0.tgz, r-oldrel (arm64): bsplinePsd_0.6.0.tgz, r-release (x86_64): bsplinePsd_0.6.0.tgz, r-oldrel (x86_64): bsplinePsd_0.6.0.tgz
Old sources:	bsplinePsd archive

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