Software Overview
MatLab CRP.
Description
Toolbox containing Matlab® routines for computing recurrence plots, cross/ joint recurrence plots and related problems.
Remarks
The CRP Toolbox for Matlab® allows for the creation of recurrence plots (RPs) as well as cross and joint recurrence plots (CRPs/ JRPs). It provides the most up-to-date quantification analysis of RPs, CRPs and JRPs (RQA), which includes the new measures of complexity as LAM and TT. Moreover, a time scale alignment tool based on CRPs is available.
Further useful tools and methods of nonlinear time series analysis and data preparation are provided:
- ACE - estimation of optimal transformations and maximal correlation;
- AR parameter estimation via Yule-Walker method;
- Fast multi-dimensional histogram estimation;
- Phase space tools (parameters, size, visualisation;
- Transformation of the data distribution to a desired distribution;
- Windowed plot of statistical parameters;
The toolbox can be used by a comfortable graphical user interface as well as on commandline (e.g. for batch computations).
Note developer: Starting with Release R2014b, Matlab contains significant changes that is causing some problems for running the CRP Toolbox in new Matlab versions. We have tried to fix several serious issues and provide an updated CRP Toolbox (R29.0) that might be mainly compatible to the Matlab releases starting with R2014b. However, due to a new but less efficient handling of objects within Matlab R2014b, the speed of some functions of the CRP Toolbox will be lowered by a factor up to 10(!). We have not yet found a good solution to speed up the code (the problem is not the implementation of the CRP Toolbox but in some Matlab internal functions related to the object handling).
At the moment it is strongly recommend to use Matlab versions up to release R2014a and to avoid R2014b (and later).
Further useful tools and methods of nonlinear time series analysis and data preparation are provided:
- ACE - estimation of optimal transformations and maximal correlation;
- AR parameter estimation via Yule-Walker method;
- Fast multi-dimensional histogram estimation;
- Phase space tools (parameters, size, visualisation;
- Transformation of the data distribution to a desired distribution;
- Windowed plot of statistical parameters;
The toolbox can be used by a comfortable graphical user interface as well as on commandline (e.g. for batch computations).
Note developer: Starting with Release R2014b, Matlab contains significant changes that is causing some problems for running the CRP Toolbox in new Matlab versions. We have tried to fix several serious issues and provide an updated CRP Toolbox (R29.0) that might be mainly compatible to the Matlab releases starting with R2014b. However, due to a new but less efficient handling of objects within Matlab R2014b, the speed of some functions of the CRP Toolbox will be lowered by a factor up to 10(!). We have not yet found a good solution to speed up the code (the problem is not the implementation of the CRP Toolbox but in some Matlab internal functions related to the object handling).
At the moment it is strongly recommend to use Matlab versions up to release R2014a and to avoid R2014b (and later).
Platform(s)
Windows
OS X (Apple Mac)
Linux
Available for
Private Devices Employees
Private Devices Students
RU-Workstations for Employees
RU-Workstations for Students
License
GNU Public License
DeploymentTypes
Radboud supported workstations: please contact the ICT Helpdesk.
Not Radboud supported workstations for Employees: please contact your IT Helpdesk.
Price on RU devices
Free
Price on private devices
Free
For students available in