R: Isolate-Detect Methodology for Multiple Change-Point Detection

# Isolate-Detect Methodology for Multiple Change-Point Detection

## Documentation for package ‘IDetect’ version 0.1.0

- DESCRIPTION file

## Help Pages

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