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 32 articles )

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  1. David Ardia; Kris Boudt; Leopoldo Catania: Generalized Autoregressive Score Models in R: The GAS Package (2019) not zbMATH
  2. Di Caterina, Claudia; Kosmidis, Ioannis: Location-adjusted Wald statistics for scalar parameters (2019)
  3. John Hughes: sklarsomega: An R Package for Measuring Agreement Using Sklar's Omega Coefficient (2018) arXiv
  4. Salehi, Mahdi; Azzalini, Adelchi: On application of the univariate Kotz distribution and some of its extensions (2018)
  5. Bradley Saul; Michael Hudgens: A Recipe for inferference: Start with Causal Inference. Add Interference. Mix Well with R. (2017) not zbMATH
  6. Marra, Giampiero; Radice, Rosalba: Bivariate copula additive models for location, scale and shape (2017)
  7. Michael Braun: sparseHessianFD: An R Package for Estimating Sparse Hessian Matrices (2017) not zbMATH
  8. Sellers, Kimberly F.; Morris, Darcy S.: Underdispersion models: models that are “under the radar” (2017)
  9. Sellers, Kimberly F.; Swift, Andrew W.; Weems, Kimberly S.: A flexible distribution class for count data (2017)
  10. Costa Mota Paraíba, Carolina; Ribeiro Diniz, Carlos Alberto: Randomly truncated nonlinear mixed-effects models (2016)
  11. David Smith and Malcolm Faddy: Mean and Variance Modeling of Under- and Overdispersed Count Data (2016) not zbMATH
  12. Eric Ghysels and Virmantas Kvedaras and Vaidotas Zemlys: Mixed Frequency Data Sampling Regression Models: The R Package midasr (2016) not zbMATH
  13. 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
  14. Nguyen, Hien D.; McLachlan, Geoffrey J.: Linear mixed models with marginally symmetric nonparametric random effects (2016)
  15. Agnieszka Król; Philippe Saint-Pierre: SemiMarkov: An R Package for Parametric Estimation in Multi-State Semi-Markov Models (2015) not zbMATH
  16. Graversen, Therese; Lauritzen, Steffen: Computational aspects of DNA mixture analysis (2015)
  17. 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)
  18. Lunardon, Nicola: Prepivoting composite score statistics by weighted bootstrap iteration (2015)
  19. Sengupta, Dishari; Choudhary, Pankaj K.; Cassey, Phillip: Modeling and analysis of method comparison data with skewness and heavy tails (2015)
  20. 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)

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