Package: nsp 1.0.0
Piotr Fryzlewicz
nsp: Inference for Multiple Change-Points in Linear Models
Implementation of Narrowest Significance Pursuit, a general and flexible methodology for automatically detecting localised regions in data sequences which each must contain a change-point (understood as an abrupt change in the parameters of an underlying linear model), at a prescribed global significance level. Narrowest Significance Pursuit works with a wide range of distributional assumptions on the errors, and yields exact desired finite-sample coverage probabilities, regardless of the form or number of the covariates. For details, see P. Fryzlewicz (2021) <https://stats.lse.ac.uk/fryzlewicz/nsp/nsp.pdf>.
Authors:
nsp_1.0.0.tar.gz
nsp_1.0.0.tar.gz(r-4.5-noble)nsp_1.0.0.tar.gz(r-4.4-noble)
nsp_1.0.0.tgz(r-4.4-emscripten)nsp_1.0.0.tgz(r-4.3-emscripten)
nsp.pdf |nsp.html✨
nsp/json (API)
# Install 'nsp' in R: |
install.packages('nsp', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:ad4f1c2996. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 05 2024 |
R-4.5-linux | OK | Dec 05 2024 |
Exports:cov_dep_multi_normcov_dep_multi_norm_polycpt_importancedraw_rectsdraw_rects_advancednspnsp_polynsp_poly_arnsp_poly_selfnormnsp_selfnormnsp_tvregsim_max_holderthresh_kab
Dependencies:lpSolve