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>.
