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). <>.

Version: 0.6.0
Imports: Rcpp (≥ 0.12.5), splines (≥ 3.2.3)
LinkingTo: Rcpp
Published: 2018-10-18
DOI: 10.32614/CRAN.package.bsplinePsd
Author: Matthew C. Edwards [aut, cre], Renate Meyer [aut], Nelson Christensen [aut]
Maintainer: Matthew C. Edwards <matt.edwards at>
License: GPL (≥ 3)
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: bsplinePsd results


Reference manual: bsplinePsd.pdf


Package source: bsplinePsd_0.6.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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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