nsp-package {nsp}R Documentation

nsp: Narrowest Significance Pursuit: Inference for Multiple Change-points in Linear Models

Description

Implementation of Narrowest Significance Pursuit (NSP), 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. NSP 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 regressors. A good place to start exploring the package are the nsp* functions.

Author(s)

Piotr Fryzlewicz, p.fryzlewicz@lse.ac.uk

References

P. Fryzlewicz (2021) "Narrowest Significance Pursuit: inference for multiple change-points in linear models", preprint.

See Also

nsp, nsp_poly, nsp_poly_ar, nsp_tvreg, nsp_selfnorm, nsp_poly_selfnorm


[Package nsp version 1.0.0 Index]