numDeriv

R package numDeriv: Accurate Numerical Derivatives. This package provide methods for calculating (usually) accurate numerical first and second order derivatives. Accurate calculations are done using Richardson’s extrapolation or, when applicable, a complex step derivative is available. A simple difference method is also provided. Simple difference is (usually) less accurate but is much quicker than Richardson’s extrapolation and provides a useful cross-check. Methods are provided for real scalar and vector valued functions.


References in zbMATH (referenced in 21 articles )

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  1. Michael Braun: sparseHessianFD: An R Package for Estimating Sparse Hessian Matrices (2017)
  2. Sellers, Kimberly F.; Morris, Darcy S.: Underdispersion models: models that are “under the radar” (2017)
  3. Sellers, Kimberly F.; Swift, Andrew W.; Weems, Kimberly S.: A flexible distribution class for count data (2017)
  4. Costa Mota Paraíba, Carolina; Ribeiro Diniz, Carlos Alberto: Randomly truncated nonlinear mixed-effects models (2016)
  5. David Smith and Malcolm Faddy: Mean and Variance Modeling of Under- and Overdispersed Count Data (2016)
  6. Eric Ghysels and Virmantas Kvedaras and Vaidotas Zemlys: Mixed Frequency Data Sampling Regression Models: The R Package midasr (2016)
  7. Fabian A. Soto, Emily Zheng, Johnny Fonseca, F. Greg Ashby: Testing separability and independence of perceptual dimensions with general recognition theory: A tutorial and new R package (grtools) (2016) arXiv
  8. Graversen, Therese; Lauritzen, Steffen: Computational aspects of DNA mixture analysis (2015)
  9. Lange, Jane M.; Hubbard, Rebecca A.; Inoue, Lurdes Y.T.; Minin, Vladimir N.: A joint model for multistate disease processes and random informative observation times, with applications to electronic medical records data (2015)
  10. Lunardon, Nicola: Prepivoting composite score statistics by weighted bootstrap iteration (2015)
  11. Sengupta, Dishari; Choudhary, Pankaj K.; Cassey, Phillip: Modeling and analysis of method comparison data with skewness and heavy tails (2015)
  12. Choudhary, Pankaj K.; Sengupta, Dishari; Cassey, Phillip: A general skew-$t$ mixed model that allows different degrees of freedom for random effects and error distributions (2014)
  13. Nash, John C.: Nonlinear parameter optimization using R tools (2014)
  14. Ruli, Erlis; Sartori, Nicola; Ventura, Laura: Marginal posterior simulation via higher-order tail area approximations (2014)
  15. Holst, Klaus Kähler; Budtz-Jørgensen, Esben: Linear latent variable models: the lava-package (2013)
  16. Emura, Takeshi; Konno, Yoshihiko: A goodness-of-fit test for parametric models based on dependently truncated data (2012)
  17. Kojadinovic, Ivan; Yan, Jun: Goodness-of-fit testing based on a weighted bootstrap: a fast large-sample alternative to the parametric bootstrap (2012)
  18. Vinod, Hrishikesh D.: Hands-on matrix algebra using R. Active and motivated learning with applications (2011)
  19. Bhatti, Chad R.: The Birnbaum-Saunders autoregressive conditional duration model (2010)
  20. Ueki, Masao; Fueda, Kaoru: Optimal tuning parameter estimation in maximum penalized likelihood method (2010)

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