CircStat: a MATLAB toolbox for circular statistics. Directional data is ubiquitious in science. Due to its circular nature such data cannot be analyzed with commonly used statistical techniques. Despite the rapid development of specialized methods for directional statistics over the last fifty years, there is only little software available that makes such methods easy to use for practioners. Most importantly, one of the most commonly used programming languages in biosciences, MATLAB, is currently not supporting directional statistics. To remedy this situation, we have implemented the CircStat toolbox for MATLAB which provides methods for the descriptive and inferential statistical analysis of directional data. We cover the statistical background of the available methods and describe how to apply them to data. Finally, we analyze a dataset from neurophysiology to demonstrate the capabilities of the CircStat toolbox.

References in zbMATH (referenced in 13 articles , 1 standard article )

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  1. Giovanna Jona Lasinio; Gianluca Mastrantonio; Mario Santoro: CircSpaceTime: an R package for spatial and spatio-temporal modeling of Circular data (2020) arXiv
  2. Storath, Martin; Weinmann, Andreas: Wavelet sparse regularization for manifold-valued data (2020)
  3. Mallot, Hanspeter A.; Lancier, Stephan: Place recognition from distant landmarks: human performance and maximum likelihood model (2018)
  4. Toth, Peter G.; Marsalek, Petr; Pokora, Ondrej: Ergodicity and parameter estimates in auditory neural circuits (2018)
  5. Lombard, F.; Hawkins, Douglas M.; Potgieter, Cornelis J.: Sequential rank CUSUM charts for angular data (2017)
  6. Matsuda, Takeru; Komaki, Fumiyasu: Time series decomposition into oscillation components and phase estimation (2017)
  7. Shirhatti, Vinay; Borthakur, Ayon; Ray, Supratim: Effect of reference scheme on power and phase of the local field potential (2016)
  8. Warrens, Matthijs J.; Pratiwi, Bunga C.: Kappa coefficients for circular classifications (2016)
  9. López-Cruz, Pedro L.; Bielza, Concha; Larrañaga, Pedro: Directional naive Bayes classifiers (2015)
  10. Mehmood, Rashid; Riaz, Muhammad; Does, Ronald J. M. M.: Efficient power computation for (r) out of (m) runs rules schemes (2013)
  11. Di Marzio, Marco; Panzera, Agnese; Taylor, Charles C.: Kernel density estimation on the torus (2011)
  12. Penev, Spiridon; Ruderman, Avraham: On the behaviour of tests based on sample spacings for moderate samples (2011)
  13. Philipp Berens: CircStat: A MATLAB Toolbox for Circular Statistics (2009) not zbMATH