Package: IDetect 0.1.1

Andreas Anastasiou

IDetect: Isolate-Detect Method for Multiple Change-Point Detection

The IDetect provides efficient implementation of the ID methodology for the consistent estimation of the number and location of multiple change-points in one-dimensional data sequences from the `deterministic + noise' model. Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, continuous piecewise-linear signal, continuous piecewise-linear signal with a heavy-tailed noise.

Authors:Andreas Anastasiou [aut, cre], Piotr Fryzlewicz [aut]

IDetect_0.1.1.tar.gz
IDetect_0.1.1.tar.gz(r-4.7-any)IDetect_0.1.1.tar.gz(r-4.6-any)
IDetect_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
IDetect/json (API)
NEWS

# Install 'IDetect' in R:
install.packages('IDetect', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.07 score 1 packages 39 scripts 208 downloads 1 mentions 17 exports 0 dependencies

Last updated from:c1ed44b7b3. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK126
source / vignettesOK167
linux-release-x86_64OK129
wasm-releaseOK90

Exports:cpt_ic_pcmcpt_ic_plmest_signalht_ID_pcmht_ID_plmIDID_pcmID_plmnormalisepcm_thplm_thresids_e_pointssol_path_pcmsol_path_plmwind_pcm_thwind_plm_th

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
IDetect: Multiple generalised change-point detection using the Isolate-Detect methodologyIDetect-package IDetect
Multiple change-point detection in the mean via minimising an information criterioncpt_ic_pcm
Multiple change-point detection in the slope of a continuous piecewise-linear mean signal via minimising an information criterioncpt_ic_plm
Calculate the contrast function that is used in continuous piecewise-linear mean signalscumsum_lin
Calculate the CUMSUM statisticcusum_function
Calculate the CUMSUM statistic at specific valuescusum_one
Estimate the signalest_signal
Apply the Isolate-Detect methodology for multiple change-point detection in the mean of a vector with non Gaussian noiseht_ID_pcm
Apply the Isolate-Detect methodology for multiple change-point detection in the slope of a vector with non Gaussian noiseht_ID_plm
Multiple change-point detection in the mean or the slope of a vector using the Isolate-Detect methodologyID
Multiple change-point detection in the mean of a vector using the Isolate-Detect methodID_pcm
Multiple change-point detection in the slope of a vector using the Isolate-Detect methodID_plm
Calculate the contrast function for the continuous piecewise-linear mean case at specific valueslinear_contr_one
Calculate the log-likelihood in the case of a continuous piecewise-linear mean signallog_lik_slope
Transform the noise to be closer to the Gaussian distributionnormalise
Multiple change-point detection in the mean via thresholdingpcm_th
Multiple change-point detection in the slope of a piecewise-linear mean signal via thresholdingplm_th
Calculate the residuals related to the estimated signalresid
Derives a subset of integers from a given sets_e_points
Schwarz Information Criterion penaltysic_pen
The solution path for the case of piecewise-constant mean signalssol_path_pcm
The solution path for the case of continuous piecewise-linear mean signalssol_path_plm
Strengthened Schwarz Information Criterion penaltyssic_pen
A window-based approach for multiple change-point detection in the mean via thresholdingwind_pcm_th
A window-based approach for multiple change-point detection in the slope of a picewise-linear mean signal via thresholdingwind_plm_th