Inference for Multiple Change-Points in Linear Models


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Documentation for package ‘nsp’ version 1.0.0

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