Segment Data With Maximum Likelihood
Segment data into exact change points
Segment data into change points assuming hierarchical structure
Segment data into change points using a mixed hierarchical-exact appro...
Efficient Logarithmic Discrete Multivariate Likelihood estimation
Print a segmentr object
Logarithmic Discrete Multivariate Likelihood estimation function imple...
Calculate a dataset's likelihood using change points of segmentr obj...
Penalize a likelihood function with a guessed penalty function
Base arguments for segment function
Segment data into change points
Given a likelihood provided by the user, this package applies it to a given matrix dataset in order to find change points in the data that maximize the sum of the likelihoods of all the segments. This package provides a handful of algorithms with different time complexities and assumption compromises so the user is able to choose the best one for the problem at hand. The implementation of the segmentation algorithms in this package are based on the paper by Bruno M. de Castro, Florencia Leonardi (2018) <arXiv:1501.01756>. The Berlin weather sample dataset was provided by Deutscher Wetterdienst <https://dwd.de/>. You can find all the references in the Acknowledgments section of this package's repository via the URL below.