A Collection of Change-Point Detection Methods


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Documentation for package ‘changepoints’ version 1.0.0

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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
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 detection via l_0 penalty.
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.
DP.poly Dynamic programming algorithm for univariate polynomials change points detection.
DP.regression Dynamic programming algorithm for regression change points detection through l_0 penalty.
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.
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.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 detection.
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.
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.
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.