A Collection of Change-Point Detection Methods

Documentation for package ‘changepoints’ version 1.1.0

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