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nsp

Inference for Multiple Change-Points in Linear Models

Installation

About

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) .

Key Metrics

Version 1.0.0
R ≥ 3.0.0
Published 2021-12-21 1023 days ago
Needs compilation? no
License GPL (≥ 3)
CRAN checks nsp results

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Maintainer

Maintainer

Piotr Fryzlewicz

Authors

Piotr Fryzlewicz

aut / cre

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Depends

R ≥ 3.0.0

Imports

lpSolve