# Inference for Multiple Change-Points in Linear Models

## Documentation for package ‘nsp’ version 1.0.0

- DESCRIPTION file

## Help Pages

cov_dep_multi_norm | Simulate covariate-dependent multiscale sup-norm for use in NSP
cov_dep_multi_norm_poly | Simulate covariate-dependent multiscale sup-norm for use in NSP, for piecewise-polynomial models
cpt_importance | Change-point importance (prominence) plot
draw_rects | Draw NSP intervals of significance as shaded rectangular areas on the current plot
draw_rects_advanced | Plot NSP intervals of significance at appropriate places along the graph of data
nsp | Narrowest Significance Pursuit algorithm with general covariates and user-specified threshold
nsp_poly | Narrowest Significance Pursuit algorithm for piecewise-polynomial signals
nsp_poly_ar | Narrowest Significance Pursuit algorithm for piecewise-polynomial signals with autoregression
nsp_poly_selfnorm | Self-normalised Narrowest Significance Pursuit algorithm for piecewise-polynomial signals
nsp_selfnorm | Self-normalised Narrowest Significance Pursuit algorithm with general covariates and user-specified threshold
nsp_tvreg | Narrowest Significance Pursuit algorithm with general covariates
sim_max_holder | Simulate Holder-like norm of the Wiener process for use in self-normalised NSP
thresh_kab | Compute the theoretical threshold for the multiscale sup-norm if the underlying distribution is standard normal