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:Piotr Fryzlewicz [aut, cre]

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'))

Peer review:

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

1.00 score 2 scripts 128 downloads 1 mentions 13 exports 1 dependencies

Last updated 3 years agofrom:ad4f1c2996. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-linuxOKNov 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