base_segment

matrix for which to find the change points

data

a function receives the segment matrix as argument
and returns a likelihood estimation. This function is used to calculate the
change points that maximize the total likelihood. Depending on the algorithm
being used, this function is likely to be executed many times, in which
case it's also likely to be the bottleneck of the function execution, so
it's advised that this function should have fast implementation.

likelihood

an integer that defines the maximum amount of segments to
split the data into.

max_segments

allows parallel execution to take place using the
registered cluster. Assumes a cluster is registered with the <code>foreach</code>
package. Defaults to TRUE.

allow_parallel

Describe base arguments for segment function

internal

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.

base_segment: Base arguments for segment function

Description

Usage

Arguments

Value