Package: fastcpd 0.14.5

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:Xingchi Li [aut, cre, cph], Xianyang Zhang [aut, cph]

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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'))

Peer review:

Bug tracker:https://github.com/doccstat/fastcpd/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • bitcoin - Bitcoin Market Price
  • occupancy - Occupancy Detection Data Set
  • transcriptome - Transcription Profiling of 57 Human Bladder Carcinoma Samples
  • uk_seatbelts - UK Seatbelts Data
  • well_log - Well-log Dataset from Numerical Bayesian Methods Applied to Signal Processing

On CRAN:

change-point-detectioncppcustom-functiongradient-descentlassolinear-regressionlogistic-regressionofflinepeltpenalized-regressionpoisson-regressionquasi-newtonstatisticstime-serieswarm-start

7.06 score 20 stars 7 scripts 759 downloads 41 exports 78 dependencies

Last updated 5 months agofrom:1f23c07ded. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 27 2024
R-4.5-win-x86_64NOTESep 27 2024
R-4.5-linux-x86_64NOTESep 27 2024
R-4.4-win-x86_64NOTESep 27 2024
R-4.4-mac-x86_64NOTESep 27 2024
R-4.4-mac-aarch64NOTESep 27 2024
R-4.3-win-x86_64NOTESep 27 2024
R-4.3-mac-x86_64NOTESep 27 2024
R-4.3-mac-aarch64NOTESep 27 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.sribriocallrclicodetoolscolorspacecrayoncurldescdiffobjdigestevaluatefansifarverfastglmforeachforecastfracdifffsgenericsggplot2glmnetgluegtablehmsisobanditeratorsjsonlitelabelinglatticelifecyclelmtestmagrittrMASSMatrixmgcvmunsellnlmennetpillarpkgbuildpkgconfigpkgloadpraiseprettyunitsprocessxprogresspsquadprogquantmodR6RColorBrewerRcppRcppArmadilloRcppClockRcppEigenrematch2rlangrprojrootscalesshapesurvivaltestthattibbletimeDatetseriesTTRurcautf8uuidvctrsviridisLitewaldowithrxtszoo

Advanced examples

Rendered fromexamples-advanced.Rmdusingknitr::rmarkdownon Sep 27 2024.

Last update: 2024-04-02
Started: 2023-12-20

Comparison with other R packages

Rendered fromcomparison-packages.Rmdusingknitr::rmarkdownon Sep 27 2024.

Last update: 2024-05-24
Started: 2023-12-20

Comparison with vanilla PELT

Rendered fromcomparison-pelt.Rmdusingknitr::rmarkdownon Sep 27 2024.

Last update: 2024-04-22
Started: 2023-12-20

Custom logistic regression model

Rendered fromexamples-custom-model.Rmdusingknitr::rmarkdownon Sep 27 2024.

Last update: 2024-04-02
Started: 2024-02-22

Readme and manuals

Help Manual

Help pageTopics
Bitcoin Market Price (USD)bitcoin
Find change points efficientlyfastcpd
Find change points efficiently in AR(p) modelsfastcpd.ar fastcpd_ar
Find change points efficiently in ARIMA(p, d, q) modelsfastcpd.arima fastcpd_arima
Find change points efficiently in ARMA(p, q) modelsfastcpd.arma fastcpd_arma
Find change points efficiently in logistic regression modelsfastcpd.binomial fastcpd_binomial
Find change points efficiently in GARCH(p, q) modelsfastcpd.garch fastcpd_garch
Find change points efficiently in penalized linear regression modelsfastcpd.lasso fastcpd_lasso
Find change points efficiently in linear regression modelsfastcpd.lm fastcpd_lm
Find change points efficiently in mean change modelsfastcpd.mean fastcpd_mean
Find change points efficiently in mean variance change modelsfastcpd.meanvariance fastcpd.mv fastcpd_meanvariance fastcpd_mv
Find change points efficiently in Poisson regression modelsfastcpd.poisson fastcpd_poisson
Find change points efficiently in time series datafastcpd.ts fastcpd_ts
Find change points efficiently in VAR(p) modelsfastcpd.var fastcpd_var
Find change points efficiently in variance change modelsfastcpd.variance fastcpd_variance
An S4 class to store the output created with 'fastcpd()'fastcpd-class
Occupancy Detection Data Setoccupancy
Plot the data and the change points for a fastcpd objectplot,fastcpd,missing-method plot.fastcpd
Print the call and the change points for a fastcpd objectprint,fastcpd-method print.fastcpd
Show the available methods for a fastcpd objectshow,fastcpd-method show.fastcpd
Show the summary of a fastcpd objectsummary,fastcpd-method summary.fastcpd
Transcription Profiling of 57 Human Bladder Carcinoma Samplestranscriptome
UK Seatbelts Datauk_seatbelts
Variance estimation for ARMA model with change pointsvariance.arma variance_arma
Variance estimation for linear models with change pointsvariance.lm variance_lm
Variance estimation for mean change modelsvariance.mean variance_mean
Variance estimation for median change modelsvariance.median variance_median
Well-log Dataset from Numerical Bayesian Methods Applied to Signal Processingwell_log