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.
- Version1.0.0
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?No
- Last release12/21/2021
Team
Piotr Fryzlewicz
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- Imports1 package