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 |
| DOI: |
10.32614/CRAN.package.nsp |
| Author: |
Piotr Fryzlewicz 
[aut, cre] |
| Maintainer: |
Piotr Fryzlewicz <p.fryzlewicz at lse.ac.uk> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| CRAN checks: |
nsp results |
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