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>.

Version: 1.0.0
Depends: R (≥ 3.0.0)
Imports: lpSolve
Published: 2021-12-21
Author: Piotr Fryzlewicz ORCID iD [aut, cre]
Maintainer: Piotr Fryzlewicz <p.fryzlewicz at lse.ac.uk>
License: GPL (≥ 3)
NeedsCompilation: no
CRAN checks: nsp results


Reference manual: nsp.pdf


Package source: nsp_1.0.0.tar.gz
Windows binaries: r-devel: nsp_1.0.0.zip, r-release: nsp_1.0.0.zip, r-oldrel: nsp_1.0.0.zip
macOS binaries: r-release (arm64): nsp_1.0.0.tgz, r-release (x86_64): nsp_1.0.0.tgz, r-oldrel: nsp_1.0.0.tgz


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