Ox

Ox is an object-oriented matrix programming language with a comprehensive mathematical and statistical function library. Matrices can be used directly in expressions, for example to multiply two matrices, or to invert a matrix. The major features of Ox are its speed, extensive library, and well-designed syntax, which leads to programs which are easier to maintain. For a first impression of the matrix and statistical function library see the Function summary. Versions of Ox are available for many platforms.


References in zbMATH (referenced in 391 articles )

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  1. Irie, Kaoru; West, Mike: Bayesian emulation for multi-step optimization in decision problems (2019)
  2. João Duarte; Vinícius Mayrink: slfm: An R Package to Evaluate Coherent Patterns in Microarray Data via Factor Analysis (2019) not zbMATH
  3. Kobayashi, Genya; Kakamu, Kazuhiko: Approximate Bayesian computation for Lorenz curves from grouped data (2019)
  4. Lima, Maria C. S.; Cordeiro, Gauss M.; Ortega, Edwin M. M.; Nascimento, Abraão D. C.: A new extended normal regression model: simulations and applications (2019)
  5. Roume, Clément; Ezzina, Samar; Blain, Hubert; Delignières, Didier: Biases in the simulation and analysis of fractal processes (2019)
  6. Chow, Sy-Miin; Ou, Lu; Ciptadi, Arridhana; Prince, Emily B.; You, Dongjun; Hunter, Michael D.; Rehg, James M.; Rozga, Agata; Messinger, Daniel S.: Representing sudden shifts in intensive dyadic interaction data using differential equation models with regime switching (2018)
  7. Darolles, Serge; Francq, Christian; Laurent, Sébastien: Asymptotics of Cholesky GARCH models and time-varying conditional betas (2018)
  8. Dey, Sanku; Mazucheli, Josmar; Nadarajah, Saralees: Kumaraswamy distribution: different methods of estimation (2018)
  9. Doornik, Jurgen A.: Accelerated estimation of switching algorithms: the cointegrated VAR model and other applications (2018)
  10. Fattore, Marco; Pelagatti, Matteo; Vittadini, Giorgio: A least squares approach to latent variables extraction in formative-reflective models (2018)
  11. Felix Pretis; J. Reade; Genaro Sucarrat: Automated General-to-Specific (GETS) Regression Modeling and Indicator Saturation for Outliers and Structural Breaks (2018) not zbMATH
  12. Imaizumi, Masaaki; Kato, Kengo: PCA-based estimation for functional linear regression with functional responses (2018)
  13. Lemonte, Artur J.; Bazán, Jorge L.: New links for binary regression: an application to coca cultivation in Peru (2018)
  14. Mazucheli, Josmar; Menezes, André F. B.; Dey, Sanku: The unit-Birnbaum-Saunders distribution with applications (2018)
  15. Melo, Tatiane F. N.; Ferrari, Silvia L. P.; Patriota, Alexandre G.: Improved estimation in a general multivariate elliptical model (2018)
  16. Pires, Juliana F.; Cysneiros, Audrey H. M. A.; Cribari-Neto, Francisco: Improved inference for the generalized Pareto distribution (2018)
  17. Alizadeh, Morad; Cordeiro, Gauss M.; Pinho, Luis Gustavo Bastos; Ghosh, Indranil: The Gompertz-G family of distributions (2017)
  18. Boudt, Kris; Laurent, Sébastien; Lunde, Asger; Quaedvlieg, Rogier; Sauri, Orimar: Positive semidefinite integrated covariance estimation, factorizations and asynchronicity (2017)
  19. Bourguignon, Marcelo; Leão, Jeremias; Leiva, Víctor; Santos-Neto, Manoel: The transmuted Birnbaum-Saunders distribution (2017)
  20. Cavaliere, Giuseppe; Nielsen, Heino Bohn; Rahbek, Anders: On the consistency of bootstrap testing for a parameter on the boundary of the parameter space (2017)

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