Package: L2hdchange 1.0

L2hdchange: L2 Inference for Change Points in High-Dimensional Time Series

Provides a method for detecting multiple change points in high-dimensional time series, targeting dense or spatially clustered signals. See Li et al. (2023) "L2 Inference for Change Points in High-Dimensional Time Series via a Two-Way MOSUM". arXiv preprint <arxiv:2208.13074>.

Authors:Jiaqi Li [aut], Likai Chen [aut], Weining Wang [aut], Wei Biao Wu [aut], Rui Lin [cre]

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L2hdchange.pdf |L2hdchange.html✨
L2hdchange/json (API)

# Install 'L2hdchange' in R:
install.packages('L2hdchange', repos = c('https://rl1081.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

On CRAN:

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

1.00 score 1 stars 184 downloads 16 exports 0 dependencies

Last updated 1 years agofrom:0d5b8eb6be. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-winOKNov 05 2024
R-4.5-linuxOKNov 05 2024
R-4.4-winOKNov 05 2024
R-4.4-macOKNov 05 2024
R-4.3-winOKNov 05 2024
R-4.3-macOKNov 05 2024

Exports:check_nbdest_hdchangegenZget_breaksget_cov_x_MAinfget_criticalget_GS_MAinfget_lr_varget_teststatsget_V_l2_MAinfhdchangeplot_resultsim_hdchange_nbdsim_hdchange_no_nbdtest_existencets_hdchange

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Check the validity of the neighbourhood specificationcheck_nbd
U.S. COVID-19 Datacovid_data
U.S. COVID-19 Data Neighbourhood Informationcovid_nbd_info
Construct an S3 class 'no_nbd' or 'nbd' for change-point estimationest_hdchange
Generate a random Gaussian vectorgenZ
Obtain the time-stamps and spatial locations with breaksget_breaks
Obtain the time-stamps and spatial locations with breaksget_breaks.nbd
Obtain the time-stamps and spatial locations without breakget_breaks.no_nbd
The covariance matrix for generating random Gaussian vectorget_cov_x_MAinf
Obtain critical values and thresholdget_critical
Obtain critical values and thresholdget_critical.nbd
Obtain critical values and thresholdget_critical.no_nbd
Obtain the simulated standardised gap vectorget_GS_MAinf
Obtain the simulated standardised gap vectorget_GS_MAinf.nbd
Obtain the simulated standardised gap vectorget_GS_MAinf.no_nbd
Compute the long-run variance of the gap vectorget_lr_var
Obtain the test statisticsget_teststats
Obtain the test statisticsget_teststats.nbd
Obtain the test statisticsget_teststats.no_nbd
Obtain the standardised gap vectorget_V_l2_MAinf
Obtain the standardised gap vectorget_V_l2_MAinf.nbd
Obtain the standardised gap vectorget_V_l2_MAinf.no_nbd
Estimate the time-stamps and spatial locations with breakshdchange
Plot the time series and change-pointsplot_result
Plot the time series and change-pointsplot_result.result_nbd
Plot the time series and change-pointsplot_result.result_no_nbd
Simulate data with neighbourhoodsim_hdchange_nbd
Simulate data without neighbourhoodsim_hdchange_no_nbd
Summarize the estimation resultssummary.result_nbd
Summarize the estimation resultssummary.result_no_nbd
Test the existence of change-points in the datatest_existence
'no_nbd' or 'nbd' object constructionts_hdchange