Package: changepoints 1.1.0

Haotian Xu

changepoints: A Collection of Change-Point Detection Methods

Performs a series of offline and/or online change-point detection algorithms for 1) univariate mean: <doi:10.1214/20-EJS1710>, <arxiv:2006.03283>; 2) univariate polynomials: <doi:10.1214/21-EJS1963>; 3) univariate and multivariate nonparametric settings: <doi:10.1214/21-EJS1809>, <doi:10.1109/TIT.2021.3130330>; 4) high-dimensional covariances: <doi:10.3150/20-BEJ1249>; 5) high-dimensional networks with and without missing values: <doi:10.1214/20-AOS1953>, <arxiv:2101.05477>, <arxiv:2110.06450>; 6) high-dimensional linear regression models: <arxiv:2010.10410>, <arxiv:2207.12453>; 7) high-dimensional vector autoregressive models: <arxiv:1909.06359>; 8) high-dimensional self exciting point processes: <arxiv:2006.03572>; 9) dependent dynamic nonparametric random dot product graphs: <arxiv:1911.07494>; 10) univariate mean against adversarial attacks: <arxiv:2105.10417>.

Authors:Haotian Xu [aut, cre], Oscar Padilla [aut], Daren Wang [aut], Mengchu Li [aut], Qin Wen [ctb]

changepoints_1.1.0.tar.gz
changepoints_1.1.0.tar.gz(r-4.5-noble)changepoints_1.1.0.tar.gz(r-4.4-noble)
changepoints_1.1.0.tgz(r-4.4-emscripten)changepoints_1.1.0.tgz(r-4.3-emscripten)
changepoints.pdf |changepoints.html✨
changepoints/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/haotianxu/changepoints/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

3.73 score 27 scripts 279 downloads 2 mentions 60 exports 26 dependencies

Last updated 2 years agofrom:2dd2969c28. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 08 2024
R-4.5-linux-x86_64NOTEOct 08 2024

Exports:aARCARCBD_UBS.covBS.uni.nonparBS.univarcalibrate.online.network.missingCI.regressionCV.search.DP.LR.regressionCV.search.DP.polyCV.search.DP.regressionCV.search.DP.univarCV.search.DP.VAR1CV.search.DPDU.regressionDP.polyDP.regressionDP.SEPPDP.univarDP.VAR1DPDU.regressiongen.cov.matgen.missinggen.piece.polygen.piece.poly.noiselessHausdorff.disthuber_meanlambda.network.missinglocal.refine.CV.VAR1local.refine.DPDU.regressionlocal.refine.networklocal.refine.polylocal.refine.regressionlocal.refine.univarlocal.refine.VAR1lowertri2matLRV.regressiononline.networkonline.network.missingonline.univaronline.univar.multisimu.change.regressionsimu.RDPGsimu.SBMsimu.SEPPsimu.VAR1softImpute.network.missingthresholdBStrim_intervaltuneBSmultinonpartuneBSnonparRDPGtuneBSuninonpartuneBSunivarWBS.intervalsWBS.multi.nonparWBS.networkWBS.nonpar.RDPGWBS.uni.nonparWBS.uni.robWBS.univarWBSIP.cov

Dependencies:codetoolsdata.treeFNNforeachgglassoglmnetiteratorskernlabKernSmoothkslatticeMASSMatrixmclustmgcvmulticoolmvtnormnlmepracmaR6RcppRcppArmadilloRcppEigenshapestringisurvival

example_univariate_mean

Rendered fromexample_univariate_mean.Rmdusingknitr::rmarkdownon Oct 08 2024.

Last update: 2022-09-04
Started: 2021-12-10

example_VAR

Rendered fromexample_VAR.Rmdusingknitr::rmarkdownon Oct 08 2024.

Last update: 2022-09-04
Started: 2021-12-10

Readme and manuals

Help Manual

Help pageTopics
Automatic adversarially robust univariate mean change point detection.aARC
Adversarially robust univariate mean change point detection.ARC
Backward detection with a robust bootstrap change point test using U-statistics for univariate mean change.BD_U
Binary Segmentation for covariance change points detection through Operator Norm.BS.cov
Standard binary segmentation for univariate nonparametric change points detection.BS.uni.nonpar
Standard binary segmentation for univariate mean change points detection.BS.univar
Calibrate step for online change point detection for network data with missing values.calibrate.online.network.missing
changepoints-package: A Collections of Change-Point Detection Methodschangepoints-package changepoints
Confidence interval construction of change points for regression settings with change points.CI.regression
Grid search based on Cross-Validation of all tuning parameters (gamma, lambda and zeta) for regression.CV.search.DP.LR.regression
Grid search for dynamic programming to select the tuning parameter through Cross-Validation.CV.search.DP.poly
Grid search based on cross-validation of dynamic programming for regression change points localisation with l_0 penalisation.CV.search.DP.regression
Grid search for dynamic programming to select the tuning parameter through Cross-Validation.CV.search.DP.univar
Grid search based on cross-validation of dynamic programming for VAR change points detection via l_0 penalty.CV.search.DP.VAR1
Grid search based on cross-validation of dynamic programming for regression change points localisation with l_0 penalisation.CV.search.DPDU.regression
Dynamic programming algorithm for univariate polynomials change points detection.DP.poly
Dynamic programming algorithm for regression change points localisation with l_0 penalisation.DP.regression
Dynamic programming for SEPP change points detection through l_0 penalty.DP.SEPP
Dynamic programming for univariate mean change points detection through l_0 penalty.DP.univar
Dynamic programming for VAR1 change points detection through l_0 penalty.DP.VAR1
Dynamic programming with dynamic update algorithm for regression change points localisation with l_0 penalisation.DPDU.regression
Generate population covariance matrix with dimension p.gen.cov.mat
Function to generate a matrix with values 0 or 1, where 0 indicating the entry is missinggen.missing
Generate univariate data from piecewise polynomials of degree at most r.gen.piece.poly
Mean function of piecewise polynomials.gen.piece.poly.noiseless
Bidirectional Hausdorff distance.Hausdorff.dist
Element-wise adaptive Huber mean estimator.huber_mean
Function to compute the default thresholding parameter for leading singular value in the soft-impute algorithm.lambda.network.missing
Local refinement for VAR1 change points detection.local.refine.CV.VAR1
Local refinement for DPDU regression change points localisation.local.refine.DPDU.regression
Local refinement for network change points detection.local.refine.network
Local refinement for univariate polynomials change point detection.local.refine.poly
Local refinement for regression change points localisation.local.refine.regression
Local refinement of an initial estimator for univariate mean change points detection.local.refine.univar
Local refinement for VAR1 change points detection.local.refine.VAR1
Transform a vector containing lower diagonal entries into a symmetric matrix of dimension p.lowertri2mat
Long-run variance estimation for regression settings with change points.LRV.regression
Online change point detection for network data.online.network
Online change point detection for network data with missing values.online.network.missing
Online change point detection with controlled false alarm rate or average run length.online.univar
Online change point detection with potentially multiple change points.online.univar.multi
Simulate a sparse regression model with change points in coefficients.simu.change.regression
Simulate a dot product graph (without change point).simu.RDPG
Simulate a Stochastic Block Model (without change point).simu.SBM
Simulate a (stable) SEPP model (without change point).simu.SEPP
Simulate from a VAR1 model (without change point).simu.VAR1
Estimate graphon matrix by soft-impute for independent adjacency matrices with missing values.softImpute.network.missing
Thresholding a BS object with threshold value tau.thresholdBS
Interval trimming based on initial change point localisation.trim_interval
A function to compute change points based on the multivariate nonparametic method with tuning parameter selected by FDR control.tuneBSmultinonpar
Change points detection for dependent dynamic random dot product graph models.tuneBSnonparRDPG
Wild binary segmentation for univariate nonparametric change points detection with tuning parameter selection.tuneBSuninonpar
Univariate mean change points detection based on standard or wild binary segmentation with tuning parameter selected by sSIC.tuneBSunivar
Generate random intervals for WBS.WBS.intervals
Wild binary segmentation for multivariate nonparametric change points detection.WBS.multi.nonpar
Wild binary segmentation for network change points detection.WBS.network
Wild binary segmentation for dependent dynamic random dot product graph models.WBS.nonpar.RDPG
Wild binary segmentation for univariate nonparametric change points detection.WBS.uni.nonpar
Robust wild binary segmentation for univariate mean change points detection.WBS.uni.rob
Wild binary segmentation for univariate mean change points detection.WBS.univar
Wild binary segmentation for covariance change points detection through Independent Projection.WBSIP.cov