NPCirc: An R package for nonparametric circular methods
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American Statistical Association
Abstract
Nonparametric density and regression estimation methods for circular data are included in the R package NPCirc. Speci cally, a circular kernel density estimation procedure
is provided, jointly with di erent alternatives for choosing the smoothing parameter. In the regression setting, nonparametric estimation for circular-linear, circular-circular and linear-circular data is also possible via the adaptation of the classical Nadaraya-Watson
and local linear estimators. In order to assess the signi cance of the features observed in
the smooth curves, both for density and regression with a circular covariate and a linear
response, a SiZer technique is developed for circular data, namely CircSiZer. Some data examples are also included in the package, jointly with a routine that allows generating mixtures of di erent circular distributions
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Oliveira, M., Crujeiras, R.M., Rodríguez-Casal, A. (2014). NPCirc: An R package for nonparametric circular methods. "Journal of Statistical Software", 61, 9 [doi: 10.18637/jss.v061.i09]
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http://dx.doi.org/10.18637/jss.v061.i09Sponsors
The authors want to acknowledge two anonymous referees for their comments which helped in improving both the paper and the package contents. The authors also acknowledge
Prof. A. Pewsey for providing the dragon
ies and cross beds data. Data from periglacial,
collected within the Project POL2006{09071 from the Spanish Ministry of Education and Science have been provided by Prof. A. P erez-Alberti. Data stored in the wind dataset are provided by NCAR/EOL under the sponsorship of the National Science Foundation.
This research has been supported by Project MTM2008{03010 from the Spanish Ministry
of Science and Innovation, and by the IAP network StUDyS (Developing crucial Statistical methods for Understanding major complex Dynamic Systems in natural, biomedical and social sciences), from Belgian Science Policy
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Attribution 3.0 Unported (CC BY 3.0)







