Package: bspec 1.6

bspec: Bayesian Spectral Inference

Bayesian inference on the (discrete) power spectrum of time series.

Authors:Christian Roever [aut, cre]

bspec_1.6.tar.gz
bspec_1.6.zip(r-4.7)bspec_1.6.zip(r-4.6)bspec_1.6.zip(r-4.5)
bspec_1.6.tgz(r-4.6-any)bspec_1.6.tgz(r-4.5-any)
bspec_1.6.tar.gz(r-4.7-any)bspec_1.6.tar.gz(r-4.6-any)
bspec_1.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
bspec/json (API)

# Install 'bspec' in R:
install.packages('bspec', repos = c('https://christianroever.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.22 score 2 packages 82 scripts 3.3k downloads 52 exports 0 dependencies

Last updated from:73c45a6aad. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK97
source / vignettesOK155
linux-release-x86_64OK104
macos-release-arm64OK188
macos-oldrel-arm64OK129
windows-develOK64
windows-releaseOK56
windows-oldrelOK86
wasm-releaseOK77

Exports:acfacf.bspecacf.defaultbspecbspec.defaultcosinewindowdposteriordposterior.bspecdpriordprior.bspecempiricalSpectrumexpectationexpectation.bspecexpectation.bspecACFhammingwindowhannwindowis.bspecis.bspecACFkaiserwindowlikelihoodlikelihood.bspecmarglikelihoodmarglikelihood.bspecmatchedfilterone.sidedone.sided.bspecplot.bspecplot.bspecACFppsampleppsample.bspecprint.bspecprint.bspecACFquantile.bspecsamplesample.bspecsample.defaultsnrsquarewindowstudenttfiltertempertemper.bspectemperaturetemperature.bspectrianglewindowtukeywindowtwo.sidedtwo.sided.bspecvariancevariance.bspecvariance.bspecACFwelchPSDwelchwindow

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Bayesian Spectral Inferencebspec-package
Posterior autocovariancesacf acf.bspec acf.default is.bspecACF plot.bspecACF print.bspecACF
Computing the spectrum's posterior distributionbspec bspec.default is.bspec plot.bspec print.bspec
Compute the "empirical" spectrum of a time series.empiricalSpectrum
Expectations and variances of distributionsexpectation expectation.bspec expectation.bspecACF variance variance.bspec variance.bspecACF
Prior, likelihood and posteriordposterior dposterior.bspec dprior dprior.bspec likelihood likelihood.bspec marglikelihood marglikelihood.bspec
Filter a noisy time series for a signal of given shapematchedfilter studenttfilter
Conversion between one- and two-sided spectraone.sided one.sided.bspec two.sided two.sided.bspec
Posterior predictive samplingppsample ppsample.bspec
Quantiles of the posterior spectrumquantile.bspec
Posterior samplingsample sample.bspec sample.default
Compute the signal-to-noise ratio (SNR) of a signalsnr
Tempering of (posterior) distributionstemper temper.bspec
Querying the tempering parametertemperature temperature.bspec
Compute windowing functions for spectral time series analysis.cosinewindow hammingwindow hannwindow kaiserwindow squarewindow trianglewindow tukeywindow welchwindow
Power spectral density estimation using Welch's method.welchPSD