Title: Isolate-Detect Methodology for Multiple Change-Point Detection
Authors@R: c(person("Andreas", "Anastasiou", email = "a.anastasiou@lse.ac.uk", role = c("aut", "cre")),
person("Piotr", "Fryzlewicz", email = "p.fryzlewicz@lse.ac.uk", role = "aut"))
Description: Provides efficient implementation of the Isolate-Detect
methodology for the consistent estimation of the number and location of multiple
change-points in one-dimensional data sequences from the "deterministic
+ noise" model. For details on the Isolate-Detect methodology, please see Anastasiou and
Currently implemented scenarios are: piecewise-constant signal with Gaussian
noise, piecewise-constant signal with heavy-tailed noise, continuous piecewise-linear
signal with Gaussian noise, continuous piecewise-linear signal with heavy-tailed noise.

Title: Assessment of Individual Identity in Animal Signals
Authors@R: person("Pavel", "Linhart", email = "pavel.linhart@centrum.cz", role = c("aut", "cre"))
Description: Provides tools for assessment and quantification of individual identity information in animal signals. This package accompanies a research article by Linhart et al. (2019) <doi:10.1101/546143>: "Measuring individual identity information in animal signals: Overview and performance of available identity metrics".

Title: Intrinsic Dimension for Data Mining
Description: Contains techniques for mining large high-dimensional data sets
by using the concept of Intrinsic Dimension (ID). Here the ID is
not necessarily integer. It is extended to fractal dimensions. And
the Morisita estimator is used for the ID estimation, but other
Note: The authors are grateful to Mikhail Kanevski, Michael Leuenberger
and Carmen D. Vega Orozco for many fruitful discussions about
the use of intrinsic dimension in data mining.

Imports: data.table, doParallel, parallel, foreach, stats, utils

Note: The authors are grateful to Mikhail Kanevski, Michael
Leuenberger, Carmen D. Vega Orozco and Fabian Guignard for many
fruitful discussions about the use of intrinsic dimension in