CircStat: AMATLABToolbox for Circular Statistics

By Philipp Berens. Journal of Statistical Software, 2009

2,018 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2ec9db187c822eed83152d3ad5bfb88919a776c7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Cognitive Neuroscience

In Molecular Biology

Citations in the corpus

Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex (2014, methodology): “CircStat, a toolbox for circular statistics, was used for all statistical analyses involving phases (20).”
Characterization of the anisotropic mechanical properties of excised human skin. (2013, methodology): “The parameter b was evaluated by the approximation given by Fisher (1993) using the CircStat package available on theMATLAB file exchange (Berens, 2009).”
The effects of shark cage-diving operations on the behaviour and movements of white sharks, Carcharodon carcharias, at the Neptune Islands, South Australia (2013, methodology): “A Watson-Williams test, analogous to a single-factor ANOVA for circular data (Berens 2009), was used to test for differences between the common mean bearing for individual shark’s COAs between operator and non-operator days and to test for sharks’ responses during periods when cage-dive operators sh”
Pingouin: statistics in Python (2018, background): “…effect sizes, as well as more advanced functions such as Bayesian T-tests (Rouder, Speckman, Sun, Morey, & Iverson, 2009), repeated measures correlations (Bakdash & Marusich, 2017), robust correlations (Pernet, Wilcox, & Rousselet, 2012) and circular statistics (Berens, 2009), to cite but a few.”
Estimating the Taylor rule in the time-frequency domain (2018, methodology): “Con dence intervals for the circular mean at each point in time were also computed we used the formulas proposed in Zar (1996), p. 604; see also Berens (2009) and the interpretation of the mean phase at each point is done considering values as extreme as the two-end points of the corresponding…”
Large-scale frontoparietal theta, alpha, and beta phase synchronization: A set of EEG differential characteristics for freezing of gait in Parkinson’s disease? (2022, methodology): “The p-value of Rayleigh’s test (function circ_rtes in Matlab) was calculated for each time point in the interval using CircStats toolbox (Berens, 2009).”
Shared resources between visual attention and visual working memory are allocated through rhythmic sampling (2019, methodology): “As an important complement to this analysis, for each of the peaks obtained with the aforementioned procedure, phase concentration among participants was tested using the Rayleigh test for non- uniformity of circular data (Fisher, 1995) as implemented in the MATLAB CircStat toolbox (Berens, 2009).”