Package: dSTEM 2.0-1

Zhibing He

dSTEM: Multiple Testing of Local Extrema for Detection of Change Points

Simultaneously detect the number and locations of change points in piecewise linear models under stationary Gaussian noise allowing autocorrelated random noise. The core idea is to transform the problem of detecting change points into the detection of local extrema (local maxima and local minima)through kernel smoothing and differentiation of the data sequence, see Cheng et al. (2020) <doi:10.1214/20-EJS1751>. A low-computational and fast algorithm call 'dSTEM' is introduced to detect change points based on the 'STEM' algorithm in D. Cheng and A. Schwartzman (2017) <doi:10.1214/16-AOS1458>.

Authors:Zhibing He <[email protected]>

dSTEM_2.0-1.tar.gz
dSTEM_2.0-1.zip(r-4.7-any)dSTEM_2.0-1.zip(r-4.6-any)
dSTEM_2.0-1.tar.gz(r-4.7-any)dSTEM_2.0-1.tar.gz(r-4.6-any)
dSTEM_2.0-1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
dSTEM/json (API)

# Install 'dSTEM' in R:
install.packages('dSTEM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • HST_stock - Stock price of Host & Hotel Resorts

On CRAN:

Conda:

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

1.00 score 3 scripts 240 downloads 13 exports 1 dependencies

Last updated from:aaab118e56. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK121
source / vignettesOK249
linux-releaseOK158
windows-develOK66
windows-releaseOK62
wasm-releaseOK112

Exports:convcp.pltcpTestdstemest.pairest.sigma2est.slopeFdrfdrBHgen.signalsmth.gausnrwhich.peaks

Dependencies:MASS