Package: fastcpd 0.14.7
fastcpd: Fast Change Point Detection via Sequential Gradient Descent
Implements fast change point detection algorithm based on the paper "Sequential Gradient Descent and Quasi-Newton's Method for Change-Point Analysis" by Xianyang Zhang, Trisha Dawn <https://proceedings.mlr.press/v206/zhang23b.html>. The algorithm is based on dynamic programming with pruning and sequential gradient descent. It is able to detect change points a magnitude faster than the vanilla Pruned Exact Linear Time(PELT). The package includes examples of linear regression, logistic regression, Poisson regression, penalized linear regression data, and whole lot more examples with custom cost function in case the user wants to use their own cost function.
Authors:
fastcpd_0.14.7.tar.gz
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fastcpd.pdf |fastcpd.html✨
fastcpd/json (API)
NEWS
# Install 'fastcpd' in R: |
install.packages('fastcpd', repos = c('https://doccstat.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/doccstat/fastcpd/issues
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change-point-detectioncppcustom-functiongradient-descentlassolinear-regressionlogistic-regressionofflinepeltpenalized-regressionpoisson-regressionquasi-newtonstatisticstime-serieswarm-start
Last updated 17 days agofrom:cc24900dca. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 05 2024 |
R-4.5-win-x86_64 | OK | Nov 05 2024 |
R-4.5-linux-x86_64 | OK | Nov 05 2024 |
R-4.4-win-x86_64 | OK | Nov 05 2024 |
R-4.4-mac-x86_64 | OK | Nov 05 2024 |
R-4.4-mac-aarch64 | OK | Nov 05 2024 |
R-4.3-win-x86_64 | OK | Nov 05 2024 |
R-4.3-mac-x86_64 | OK | Nov 05 2024 |
R-4.3-mac-aarch64 | OK | Nov 05 2024 |
Exports:fastcpdfastcpd_arfastcpd_arimafastcpd_armafastcpd_binomialfastcpd_garchfastcpd_lassofastcpd_lmfastcpd_meanfastcpd_meanvariancefastcpd_mvfastcpd_poissonfastcpd_tsfastcpd_varfastcpd_variancefastcpd.arfastcpd.arimafastcpd.armafastcpd.binomialfastcpd.garchfastcpd.lassofastcpd.lmfastcpd.meanfastcpd.meanvariancefastcpd.mvfastcpd.poissonfastcpd.tsfastcpd.varfastcpd.varianceplotprintshowsummaryvariance_armavariance_lmvariance_meanvariance_medianvariance.armavariance.lmvariance.meanvariance.median
Dependencies:BHbigmemorybigmemory.sribriocallrclicodetoolscolorspacecrayoncurldescdiffobjdigestevaluatefansifarverfastglmforeachforecastfracdifffsgenericsggplot2glmnetgluegtablehmsisobanditeratorsjsonlitelabelinglatticelifecyclelmtestmagrittrMASSMatrixmgcvmunsellnlmennetpillarpkgbuildpkgconfigpkgloadpraiseprettyunitsprocessxprogresspsquadprogquantmodR6RColorBrewerRcppRcppArmadilloRcppClockRcppEigenrlangrprojrootscalesshapesurvivaltestthattibbletimeDatetseriesTTRurcautf8uuidvctrsviridisLitewaldowithrxtszoo
Advanced examples
Rendered fromexamples-advanced.Rmd
usingknitr::rmarkdown
on Nov 05 2024.Last update: 2024-04-02
Started: 2023-12-20
Comparison with other R packages
Rendered fromcomparison-packages.Rmd
usingknitr::rmarkdown
on Nov 05 2024.Last update: 2024-11-04
Started: 2023-12-20
Comparison with vanilla PELT
Rendered fromcomparison-pelt.Rmd
usingknitr::rmarkdown
on Nov 05 2024.Last update: 2024-04-22
Started: 2023-12-20
Custom logistic regression model
Rendered fromexamples-custom-model.Rmd
usingknitr::rmarkdown
on Nov 05 2024.Last update: 2024-04-02
Started: 2024-02-22