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. |