A Software Tool for the Exponential Power Distribution: The normalp Package. In this paper we present the normalp package, a package for the statistical environment R that has a set of tools for dealing with the exponential power distribution. In this package there are functions to compute the density function, the distribution function and the quantiles from an exponential power distribution and to generate pseudo-random numbers from the same distribution. Moreover, methods concerning the estimation of the distribution parameters are described and implemented. It is also possible to estimate linear regression models when we assume the random errors distributed according to an exponential power distribution. A set of functions is designed to perform simulation studies to see the suitability of the estimators used. Some examples of use of this package are provided.

This software is also peer reviewed by journal JSS.

References in zbMATH (referenced in 16 articles , 1 standard article )

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  1. Çankaya, Mehmet Niyazi; Yalçınkaya, Abdullah; Altındaǧ, Ömer; Arslan, Olcay: On the robustness of an Epsilon skew extension for Burr III distribution on the real line (2019)
  2. Parreira da Silva, Guilherme; Taconeli, Cesar Augusto; Zeviani, Walmes Marques; do Nascimento, Isadora Aparecida Sprengoski: Performance of Shewhart control charts based on neoteric ranked set sampling to monitor the process mean for normal and non-normal processes (2019)
  3. Barnard, Roger W.; Pearce, Kent; Trindade, A. Alexandre: When is tail mean estimation more efficient than tail median? Answers and implications for quantitative risk management (2018)
  4. Awadalla, Saria Salah; Mudholkar, Govind S.; Yu, Ziji: The power M-Gaussian distribution: an R-symmetric analog of the exponential-power distribution (2017)
  5. Hafner, Christian M.; Linton, Oliver: An almost closed form estimator for the EGARCH model (2017)
  6. Tumlinson, S. E.; Keating, J. P.; Balakrishnan, N.: Linear estimation for the extended exponential power distribution (2016)
  7. Richter, Wolf-Dieter: Convex and radially concave contoured distributions (2015)
  8. Paige, Robert L.; Trindade, A. Alexandre; Wickramasinghe, R. Indika P.: Extensions of saddlepoint-based bootstrap inference (2014)
  9. Coin, Daniele: A method to estimate power parameter in exponential power distribution via polynomial regression (2013)
  10. Li, Xinmin; Tian, Lili; Wang, Juan; Muindi, Josephia R.: Comparison of quantiles for several normal populations (2012)
  11. Tarsitano, Agostino; Falcone, Marianna: Missing-values adjustment for mixed-type data (2011)
  12. Bolker, Benjamin M.: Evolution of dispersal scale and shape in heterogeneous environments: a correlation equation approach (2010)
  13. Lama, Nicola; Boracchi, Patrizia; Biganzoli, Elia: Exploration of distributional models for a novel intensity-dependent normalization procedure in censored gene expression data (2009)
  14. Martín, J.; Pérez, C. J.: Bayesian analysis of a generalized lognormal distribution (2009)
  15. Víctor Leiva;Hugo Hernández; Antonio Sanhueza: An R Package for a General Class of Inverse Gaussian Distributions (2008) not zbMATH
  16. Angelo Mineo; Mariantonietta Ruggieri: A Software Tool for the Exponential Power Distribution: The normalp Package (2005) not zbMATH