Maxlik

Maxlik: a package for maximum likelihood estimation in R. This paper describes the package maxLik for the statistical environment R. The package is essentially a unified wrapper interface to various optimization routines, offering easy access to likelihood-specific features like standard errors or information matrix equality (BHHH method). More advanced features of the optimization algorithms, such as forcing the value of a particular parameter to be fixed, are also supported.


References in zbMATH (referenced in 24 articles , 1 standard article )

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  1. Frery, Alejandro C.; Gambini, Juliana: Comparing samples from the (\mathcalG^0) distribution using a geodesic distance (2020)
  2. Diallo, Alpha Oumar; Diop, Aliou; Dupuy, Jean-François: Estimation in zero-inflated binomial regression with missing covariates (2019)
  3. Espinosa, Javier; Hennig, Christian: A constrained regression model for an ordinal response with ordinal predictors (2019)
  4. Graf, Monique; Marín, J. Miguel; Molina, Isabel: A generalized mixed model for skewed distributions applied to small area estimation (2019)
  5. Padayachee, Trishanta; Khamiakova, Tatsiana; Shkedy, Ziv; Salo, Perttu; Perola, Markus; Burzykowski, Tomasz: A multivariate linear model for investigating the association between gene-module co-expression and a continuous covariate (2019)
  6. Santana, Tiago V. F.; Ortega, Edwin M. M.; Cordeiro, Gauss M.: Generalized beta Weibull linear model: estimation, diagnostic tools and residual analysis (2019)
  7. Aragon, Davi C.; Achcar, Jorge A.; Martinez, Edson Z.: Maximum likelihood and Bayesian estimators for the double Poisson distribution (2018)
  8. Diallo, Alpha Oumar; Diop, Aliou; Dupuy, Jean-François: Analysis of multinomial counts with joint zero-inflation, with an application to health economics (2018)
  9. Louzada, Francisco; Shimizu, Taciana K. O.; Suzuki, Adriano K.; Mazucheli, Josmar; Ferreira, Paulo H.: Compositional regression modeling under tilted normal errors: an application to a Brazilian Super League Volleyball data set (2018)
  10. Pruim, Randall: Foundations and applications of statistics. An introduction using R (2018)
  11. Yu, Jun; Kong, Xiangshun; Ai, Mingyao; Tsui, Kwok Leung: Optimal designs for dose-response models with linear effects of covariates (2018)
  12. Emanuele Giorgi and Peter Diggle: PrevMap: An R Package for Prevalence Mapping (2017) not zbMATH
  13. Godwin, Ryan T.: One-inflation and unobserved heterogeneity in population size estimation (2017)
  14. Lee, Kyungsub; Seo, Byoung Ki: Modeling microstructure price dynamics with symmetric Hawkes and diffusion model using ultra-high-frequency stock data (2017)
  15. Kosmidis, Ioannis; Karlis, Dimitris: Model-based clustering using copulas with applications (2016)
  16. Salehi, Mahdi; Doostparast, Mahdi: Expressions for moments of order statistics and records from the skew-normal distribution in terms of multivariate normal orthant probabilities (2015)
  17. Wright, Marvin N.; Ziegler, Andreas: Multiple censored data in dentistry: a new statistical model for analyzing lesion size in randomized controlled trials (2015)
  18. Manisera, Marica; Zuccolotto, Paola: Modeling rating data with nonlinear CUB models (2014)
  19. Millo, Giovanni: Maximum likelihood estimation of spatially and serially correlated panels with random effects (2014)
  20. Nash, John C.: Nonlinear parameter optimization using R tools (2014)

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