CptNonPar 0.3.1

• By default, the data is now centered and scaled before change point detection is applied, which improves empirical performance. This can be turned off by setting the new argument scale.data = FALSE in the functions np.mojo(), np.mojo.multilag(), and multiscale.np.mojo().

CptNonPar 0.3.0

• The associated paper is now accepted for publication in Biometrika: see
  doi:10.1093/biomet/asaf024 for full details.
• Updated multiscale.np.mojo() function so that returned cpts are given in
  time order.
• The p-values returned by np.mojo(), np.mojo.multilag(), and
  mulsticale.np.mojo() have been replaced by importance scores: when the
  bootstrap is used, these are essentially one minus the p-value.
  Larger scores give more evidence of a change point.
• Fixed error that occurs when using the manual threshold and the
  np.mojo.multilag() function, thanks to Chuanyang Zhang for spotting this.
• You can now use different manual thresholds for different lags: for use
  in np.mojo.multilag(), you can supply a vector, whilst for
  mulsticale.np.mojo(), you can supply a list of vectors.
• Added package level documentation: see ?CptNonPar.

CptNonPar 0.2.1

*Updated link to the paper in description to comply with CRAN check.

CptNonPar 0.1.1

• Updated description field in Description file.
• Updated examples in np.mojo, np.mojo.multilag, and multilag.cpts.merge.