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:
dSTEM_2.0-1.tar.gz
dSTEM_2.0-1.tar.gz(r-4.5-noble)dSTEM_2.0-1.tar.gz(r-4.4-noble)
dSTEM_2.0-1.tgz(r-4.4-emscripten)dSTEM_2.0-1.tgz(r-4.3-emscripten)
dSTEM.pdf |dSTEM.html✨
dSTEM/json (API)
NEWS
| # Install 'dSTEM' in R: |
| install.packages('dSTEM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- HST_stock - Stock price of Host & Hotel Resorts
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:aaab118e56. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 03 2024 |
| R-4.5-linux | OK | Dec 03 2024 |
Exports:convcp.pltcpTestdstemest.pairest.sigma2est.slopeFdrfdrBHgen.signalsmth.gausnrwhich.peaks
Dependencies:MASS
