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NPCirc: An R Package for Nonparametric Circular Methods

Author

Listed:
  • Oliveira, María
  • Crujeiras, Rosa M.
  • Rodríguez-Casal, Alberto

Abstract

Nonparametric density and regression estimation methods for circular data are included in the R package NPCirc. Specifically, a circular kernel density estimation procedure is provided, jointly with different 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 significance 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 different circular distributions.

Suggested Citation

  • Oliveira, María & Crujeiras, Rosa M. & Rodríguez-Casal, Alberto, 2014. "NPCirc: An R Package for Nonparametric Circular Methods," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i09).
    • Handle: RePEc:jss:jstsof:v:061:i09
    DOI: http://hdl.handle.net/10.18637/jss.v061.i09
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    References listed on IDEAS

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    1. Taylor, Charles C., 2008. "Automatic bandwidth selection for circular density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3493-3500, March.
    2. Di Marzio, Marco & Panzera, Agnese & Taylor, Charles C., 2009. "Local polynomial regression for circular predictors," Statistics & Probability Letters, Elsevier, vol. 79(19), pages 2066-2075, October.
    3. Feng, Dai & Tierney, Luke, 2008. "Computing and Displaying Isosurfaces in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i01).
    4. A. Mooney, Jennifer & Helms, Peter J. & Jolliffe, Ian T., 2003. "Fitting mixtures of von Mises distributions: a case study involving sudden infant death syndrome," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 505-513, January.
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    Cited by:

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    2. Paula Saavedra-Nieves & Rosa M. Crujeiras, 2022. "Nonparametric estimation of directional highest density regions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(3), pages 761-796, September.
    3. Han, Qinkai & Hao, Zhuolin & Hu, Tao & Chu, Fulei, 2018. "Non-parametric models for joint probabilistic distributions of wind speed and direction data," Renewable Energy, Elsevier, vol. 126(C), pages 1032-1042.
    4. Zihao Wu & Carolina Euan & Rosa M. Crujeiras & Ying Sun, 2023. "Estimation and Clustering of Directional Wave Spectra," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(3), pages 502-525, September.
    5. Bedouhene Kahina & Zougab Nabil, 2020. "A Bayesian procedure for bandwidth selection in circular kernel density estimation," Monte Carlo Methods and Applications, De Gruyter, vol. 26(1), pages 69-82, March.
    6. Han, Qinkai & Chu, Fulei, 2021. "Directional wind energy assessment of China based on nonparametric copula models," Renewable Energy, Elsevier, vol. 164(C), pages 1334-1349.

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