Title: Nonparametric Change Detection in Multivariate Linear
Author: Olga Gorskikh, Pekka Malo, Pauliina Ilmonen, Lauri Viitasaari, Joni Virta
Description: Contains implementation of the Nonparametric Splitting Algorithm (NSA), which estimates a set of structural change points (change dates) within a multivariate time-wise linear regression. Additionally, it contains utility functions to estimate corresponding changing linear model, moving energy distance and a change-detection test. For more information, see Malo et. al (2019) <arXiv:1805.08512>.

Description: Contains implementation of the Nonparametric Splitting Algorithm (NSA), which estimates a set of structural change points (change dates) within a multivariate time-wise linear regression. Additionally, it contains utility functions to estimate corresponding changing linear model, moving energy distance and a change-detection test. For more information, see Malo et. al (2019) <arXiv:1805.08512v2>.
Packaged: 2019-06-17 09:37:40 UTC; olga.gorskikh

Title: Contains funcions that run various single and multiple
Author: Rebecca Killick <r.killick@lancs.ac.uk>, Idris A. Eckley
Description: Implements various mainstream and specialised changepoint
methods for finding single and multiple changepoints within
data.  Many popular non-parametric and frequentist methods are
included.  The cpt.mean, cpt.var, cpt.meanvar, cpt.reg
functions should be your first point of call.
Depends: R(>= 2.11), methods, graphics, base

included.  The cpt.mean, cpt.var, cpt.meanvar functions should

Title: An R package for changepoint analysis

Author: Rebecca Killick <r.killick@lancs.ac.uk> and Idris Eckley with

Description: Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data.  Many popular non-parametric and frequentist methods are included.  The cpt.mean, cpt.var, cpt.meanvar functions should be your first point of call.

Title: An R Package for Changepoint Analysis

Description: Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data.  Many popular non-parametric and frequentist methods are included.  The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call.