Package: stepR 2.1-10
stepR: Multiscale Change-Point Inference
Allows fitting of step-functions to univariate serial data where neither the number of jumps nor their positions is known by implementing the multiscale regression estimators SMUCE, simulataneous multiscale changepoint estimator, (K. Frick, A. Munk and H. Sieling, 2014) <doi:10.1111/rssb.12047> and HSMUCE, heterogeneous SMUCE, (F. Pein, H. Sieling and A. Munk, 2017) <doi:10.1111/rssb.12202>. In addition, confidence intervals for the change-point locations and bands for the unknown signal can be obtained.
Authors:
stepR_2.1-10.tar.gz
stepR_2.1-10.tar.gz(r-4.5-noble)stepR_2.1-10.tar.gz(r-4.4-noble)
stepR_2.1-10.tgz(r-4.4-emscripten)stepR_2.1-10.tgz(r-4.3-emscripten)
stepR.pdf |stepR.html✨
stepR/json (API)
# Install 'stepR' in R: |
install.packages('stepR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- MRC.1000 - Values of the MRC statistic for 1,000 observations
- MRC.asymptotic - "Asymptotic" values of the MRC statistic
- MRC.asymptotic.dyadic - "Asymptotic" values of the MRC statistic
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 months agofrom:de8f89bd35. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 18 2024 |
R-4.5-linux-x86_64 | OK | Nov 18 2024 |
Exports:.testSmallScalesBesselPolynomialboundsbounds.MRCchichi.FFTcompareBlockscomputeBoundscomputeStatconfbandcontMCcritValdfilterjsmurfjumpintkMRC.pvaluekMRC.quantkMRC.simulmonteCarloSimulationMRCMRC.FFTMRC.pvalueMRC.quantMRC.simulMRCoeffMRCoeff.FFTneighbourssdrobnormsmuceRstepblockstepboundstepbound.defaultstepbound.stepcandstepcandstepfitstepFitsteppathsteppath.defaultsteppath.stepcandstepselstepsel.AICstepsel.BICstepsel.MRCthresh.smuceRtransit
Dependencies:digestlowpassFilterR.cacheR.methodsS3R.ooR.utilsRcpp