Title: Methods for Changepoint Detection
Depends: R(>= 3.0), methods, stats, zoo(>= 0.9-1)

Depends: R(>= 3.1), methods, stats, zoo(>= 0.9-1)

Title: Geometrically Inspired Multivariate Changepoint Detection
Description: Implements the high-dimensional changepoint detection method GeomCP and the related mappings used for changepoint detection. These methods view the changepoint problem from a geometrical viewpoint and aim to extract relevant geometrical features in order to detect changepoints. The geomcp() function should be your first point of call. References: Grundy et al. (2020) <doi:10.1007/s11222-020-09940-y>.
Depends: R(>= 3.6), changepoint, changepoint.np, methods, ggplot2

Title: Package to Calculate the Influence of the Data on a Changepoint
Imports: data.table, ggplot2, gridExtra, reshape, graphics, methods
Description: Allows users to input their data, segmentation and function used for the segmentation (and additional arguments) and the package calculates the influence of the data on the changepoint locations, see Wilms et al. (2021) <arXiv:2107.10572>.  Currently this can only be used with the changepoint package functions to identify changes, but we plan to extend this.  There are options for different types of graphics to assess the influence.

Title: Changepoint Analysis for Multivariate Time Series
Author: Lawrence Bardwell <l.bardwell@lancaster.ac.uk> [aut], Idris Eckley <i.eckley@lancaster.ac.uk> [ths,ctb], Paul Fearnhead <p.fearnhead@lancaster.ac.uk> [ths,ctb], Daniel Grose <dan.grose@lancaster.ac.uk> [aut,cre]
Description: Detects the Most Recent Changepoints (mrc) for panel data consisting of many related univariate timeseries.

Description: Detects the Most Recent Changepoints (mrc) for panel data consisting of many related univariate timeseries using the method developed by Bardwell, Fearnhead, Eckley, Smith and Spott (2018) <doi:10.1080/00401706.2018.1438926>.

Title: Methods for Nonparametric Changepoint Detection
Author: c(person("Kaylea", "Haynes", role = c("aut", "cre"), email =
person("Paul", "Fearnhead", role=c("ths", "ctr")), person("Idris", "Eckley",
Description: Implements the multiple changepoint algorithm PELT with a
nonparametric cost function based on the empirical distribution of the data. The cpt.np() function should be your first point of call.
This package is an extension to the \code{changepoint} package which uses parametric changepoint methods.  For further information on the methods see the