DEoptim

DEoptim: An R Package for Global Optimization by Differential Evolution. This article describes the R package DEoptim which implements the differential evolution algorithm for the global optimization of a real-valued function of a real-valued parameter vector. The implementation of differential evolution in DEoptim interfaces with C code for efficiency. The utility of the package is illustrated via case studies in fitting a Parratt model for X-ray reflectometry data and a Markov-Switching Generalized AutoRegressive Conditional Heteroskedasticity (MSGARCH) model for the returns of the Swiss Market Index.


References in zbMATH (referenced in 54 articles , 1 standard article )

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  1. Ghitany, Mohamed E.; Aboukhamseen, Suja M.; Baqer, Abrar A.; Gupta, Ramesh C.: Gompertz-Lindley distribution and associated inference (2022)
  2. Mohammadi, Hossein; Challenor, Peter; Williamson, Daniel; Goodfellow, Marc: Cross-validation-based adaptive sampling for Gaussian process models (2022)
  3. Ouédraogo, Ouindllassida Jean-Etienne: A simple and efficient method for fitting the three-parameter gamma distribution to given data (2022)
  4. Shao, Wei; Zuo, Yijun; Luo, June: Employing the MCMC technique to compute the projection depth in high dimensions (2022)
  5. You, Kisung; Suh, Changhee: Parameter estimation and model-based clustering with spherical normal distribution on the unit hypersphere (2022)
  6. Augustyniak, Maciej; Godin, Frédéric; Hamel, Emmanuel: A mixed bond and equity fund model for the valuation of variable annuities (2021)
  7. Ouédraogo, Ouindllassida Jean-Etienne; Katchekpele, Edoh; Kpanzou, Tchilabalo Abozou: Adaptative bounds and estimation in the case of the three-parameter Weibull distribution (2021)
  8. Ozan Evkaya, O.; Yozgatlıgil, Ceylan; Sevtap Selcuk-Kestel, A.: CD-vine model for capturing complex dependence (2021)
  9. Alexander Lange, Bernhard Dalheimer, Helmut Herwartz, Simone Maxand: svars: An R Package for Data-Driven Identification in Multivariate Time Series Analysis (2020) not zbMATH
  10. Boudt, Kris; Wan, Chunlin: The effect of velocity sparsity on the performance of cardinality constrained particle swarm optimization (2020)
  11. He, Yunhao; Leippold, Markus: Short-run risk, business cycle, and the value premium (2020)
  12. Kleisinger-Yu, Xi; Komaric, Vlatka; Larsson, Martin; Regez, Markus: A multifactor polynomial framework for long-term electricity forwards with delivery period (2020)
  13. Shakhatreh, Mohammed K.; Lemonte, Artur J.; Cordeiro, Gauss M.: On the generalized extended exponential-Weibull distribution: properties and different methods of estimation (2020)
  14. Trindade, Marco A. S.; Floquet, Sergio; Filho, Lourival M. Silva: Portfolio theory, information theory and Tsallis statistics (2020)
  15. Blostein, Martin; Miljkovic, Tatjana: On modeling left-truncated loss data using mixtures of distributions (2019)
  16. Castillo-Páez, Sergio; Fernández-Casal, Rubén; García-Soidán, Pilar: A nonparametric bootstrap method for spatial data (2019)
  17. David Ardia; Keven Bluteau; Kris Boudt; Leopoldo Catania; Denis-Alexandre Trottier: Markov-Switching GARCH Models in R: The MSGARCH Package (2019) not zbMATH
  18. David Ardia; Kris Boudt; Leopoldo Catania: Generalized Autoregressive Score Models in R: The GAS Package (2019) not zbMATH
  19. Cano-Berlanga, Sebastián; Giménez-Gómez, José-Manuel: On Chinese stock markets: how have they evolved over time? (2018)
  20. Dünder, Emre; Gümüştekin, Serpil; Murat, Naci; Cengiz, Mehmet Ali: Variable selection in linear regression analysis with alternative Bayesian information criteria using differential evaluation algorithm (2018)

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