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 63 articles )

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  1. Arsham, Aryana; Bebu, Ionut; Mathew, Thomas: A bivariate regression-based cost-effectiveness analysis (2022)
  2. Baumgartner, Matheus Tenório; Faria, Lucas Del Bianco: The sensitivity of complex dynamic food webs to the loss of top omnivores (2022)
  3. Berg, Sergey S.; Palmer, Laura L.: A comparison of multinomial likelihood and chi-square approaches to statistical population reconstruction (2021)
  4. Columbu, Silvia; Mameli, Valentina; Musio, Monica; Dawid, Philip: The Hyvärinen scoring rule in Gaussian linear time series models (2021)
  5. Cristina Tortora, Ryan P. Browne, Aisha ElSherbiny, Brian C. Franczak, Paul D. McNicholas: Model-Based Clustering, Classification, and Discriminant Analysis Using the Generalized Hyperbolic Distribution: MixGHD R package (2021) not zbMATH
  6. Gómez, Yolanda M.; Gallardo, Diego I.; de Castro, Mário: A regression model for positive data based on the slashed half-normal distribution (2021)
  7. Hartmann, Raphael; Klauer, Karl Christoph: Partial derivatives for the first-passage time distribution in Wiener diffusion models (2021)
  8. Lin, Huihui; Chaganty, N. Rao: Multivariate distributions of correlated binary variables generated by pair-copulas (2021)
  9. Zhang, Shulin; Zhou, Qian M.; Lin, Huazhen: Goodness-of-fit test of copula functions for semi-parametric univariate time series models (2021)
  10. Lisa Amrhein, Christiane Fuchs: stochprofML: Stochastic Profiling Using Maximum Likelihood Estimation in R (2020) arXiv
  11. Alireza S. Mahani; Mansour T.A. Sharabiani: Bayesian, and Non-Bayesian, Cause-Specific Competing-Risk Analysis for Parametric and Nonparametric Survival Functions: The R Package CFC (2019) not zbMATH
  12. David Ardia; Keven Bluteau; Kris Boudt; Leopoldo Catania; Denis-Alexandre Trottier: Markov-Switching GARCH Models in R: The MSGARCH Package (2019) not zbMATH
  13. David Ardia; Kris Boudt; Leopoldo Catania: Generalized Autoregressive Score Models in R: The GAS Package (2019) not zbMATH
  14. David Smith; Malcolm Faddy: Mean and Variance Modeling of Under-Dispersed and Over-Dispersed Grouped Binary Data (2019) not zbMATH
  15. Di Caterina, Claudia; Kosmidis, Ioannis: Location-adjusted Wald statistics for scalar parameters (2019)
  16. Grün, Bettina; Miljkovic, Tatjana: Extending composite loss models using a general framework of advanced computational tools (2019)
  17. Hirk, Rainer; Hornik, Kurt; Vana, Laura: Multivariate ordinal regression models: an analysis of corporate credit ratings (2019)
  18. Lai, Keke: Creating misspecified models in moment structure analysis (2019)
  19. Sellers, Kimberly F.; Young, Derek S.: Zero-inflated sum of Conway-Maxwell-poissons (ZISCMP) regression (2019)
  20. Stojkova, Biljana Jonoska; Campbell, David A.: Incremental mixture importance sampling with shotgun optimization (2019)

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