Package: bspec 1.6
bspec: Bayesian Spectral Inference
Bayesian inference on the (discrete) power spectrum of time series.
Authors:
bspec_1.6.tar.gz
bspec_1.6.zip(r-4.5)bspec_1.6.zip(r-4.4)bspec_1.6.zip(r-4.3)
bspec_1.6.tgz(r-4.4-any)bspec_1.6.tgz(r-4.3-any)
bspec_1.6.tar.gz(r-4.5-noble)bspec_1.6.tar.gz(r-4.4-noble)
bspec_1.6.tgz(r-4.4-emscripten)bspec_1.6.tgz(r-4.3-emscripten)
bspec.pdf |bspec.html✨
bspec/json (API)
| # Install 'bspec' in R: |
| install.packages('bspec', repos = c('https://christianroever.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:73c45a6aad. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 17 2024 |
| R-4.5-win | OK | Sep 17 2024 |
| R-4.5-linux | OK | Sep 17 2024 |
| R-4.4-win | OK | Sep 17 2024 |
| R-4.4-mac | OK | Sep 17 2024 |
| R-4.3-win | OK | Sep 17 2024 |
| R-4.3-mac | OK | Sep 17 2024 |
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 page | Topics |
|---|---|
| Bayesian Spectral Inference | bspec-package |
| Posterior autocovariances | acf acf.bspec acf.default is.bspecACF plot.bspecACF print.bspecACF |
| Computing the spectrum's posterior distribution | bspec bspec.default is.bspec plot.bspec print.bspec |
| Compute the "empirical" spectrum of a time series. | empiricalSpectrum |
| Expectations and variances of distributions | expectation expectation.bspec expectation.bspecACF variance variance.bspec variance.bspecACF |
| Prior, likelihood and posterior | dposterior dposterior.bspec dprior dprior.bspec likelihood likelihood.bspec marglikelihood marglikelihood.bspec |
| Filter a noisy time series for a signal of given shape | matchedfilter studenttfilter |
| Conversion between one- and two-sided spectra | one.sided one.sided.bspec two.sided two.sided.bspec |
| Posterior predictive sampling | ppsample ppsample.bspec |
| Quantiles of the posterior spectrum | quantile.bspec |
| Posterior sampling | sample sample.bspec sample.default |
| Compute the signal-to-noise ratio (SNR) of a signal | snr |
| Tempering of (posterior) distributions | temper temper.bspec |
| Querying the tempering parameter | temperature 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 |
