Isolate-Detect Methodology for Multiple Change-Point Detection

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Documentation for package ‘IDetect’ version 0.1.0

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IDetect-package IDetect: Multiple generalised change-point detection using the Isolate-Detect methodology
cplm_ic Multiple change-point detection in a continuous piecewise-linear signal via minimising an information criterion
cplm_th Multiple change-point detection in a continuous, piecewise-linear signal via thresholding
est_signal Estimate the signal
ht_ID_cplm Apply the Isolate-Detect methodology for multiple change-point detection in a continuous, piecewise-linear vector with non Gaussian noise
ht_ID_pcm Apply the Isolate-Detect methodology for multiple change-point detection in the mean of a vector with non Gaussian noise
ID Multiple change-point detection in piecewise-constant or continuous, piecewise-linear signals using the Isolate-Detect methodology
IDetect IDetect: Multiple generalised change-point detection using the Isolate-Detect methodology
ID_cplm Multiple change-point detection for a continuous, piecewise-linear signal using the Isolate-Detect methodology
ID_pcm Multiple change-point detection in the mean of a vector using the Isolate-Detect methodology
normalise Transform the noise to be closer to the Gaussian distribution
pcm_ic Multiple change-point detection in the mean via minimising an information criterion
pcm_th Multiple change-point detection in the mean via thresholding
resid_ID Calculate the residuals related to the estimated signal
sol_path_cplm The solution path for the case of continuous piecewise-linear signals
sol_path_pcm The solution path for the case of piecewise-constant signals
s_e_points Derives a subset of integers from a given set
win_cplm_th A windows-based approach for multiple change-point detection in a continuous, piecewise-linear signal via thresholding
win_pcm_th A windows-based approach for multiple change-point detection in the mean via thresholding