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Software to accompany P. Fryzlewicz (2020) "Narrowest Significance Pursuit: inference for multiple change-points in linear models".

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nsp

Software to accompany P. Fryzlewicz (2020) "Narrowest Significance Pursuit: inference for multiple change-points in linear models" and P. Fryzlewicz (2021) "Robust Narrowest Significance Pursuit: inference for multiple change-points in the median"

(A) "Narrowest Significance Pursuit: inference for multiple change-points in linear models"

NOTE: the code for part (A) has now been turned into an R package called "nsp" and made available on CRAN here.

The entire content of this repository will be removed once (B) has also been incorporated into "nsp".

Please do not use the code described in part (A) here. Use the "nsp" package instead.

(Legacy description starts here.) The main files are

  • NSP_for_Github_v*.R (the actual software), and
  • NSP_simulations_and_data_examples_v*.R (as in the title).

To use NSP_for_Github_v*.R, do the following:

  • Install the R package lpSolve.
  • Save wiener_holder_norms.txt, tight_mres_norms_const.RData, tight_mres_norms_lin.RData to your R working directory.
  • Source NSP_for_Github_v*.R into R.
  • Read the descriptions within NSP_for_Github_v*.R.

Requires R package lpSolve. (Legacy description ends here.)

(B) "Robust Narrowest Significance Pursuit: inference for multiple change-points in the median"

The main file is

  • RNSP_for_Github_v*.R (the actual software)

This content will be turned into an R package and posted on CRAN at the earliest opportunity.

Questions/comments? - p.fryzlewicz@lse.ac.uk (Piotr Fryzlewicz)

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Software to accompany P. Fryzlewicz (2020) "Narrowest Significance Pursuit: inference for multiple change-points in linear models".

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