# A Collection of Change-Point Detection Methods

## Documentation for package ‘changepoints’ version 1.1.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

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.