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 443 articles )

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  1. Araújo, Mariana C.; Cysneiros, Audrey H. M. A.; Montenegro, Lourdes C.: Improved heteroskedasticity likelihood ratio tests in symmetric nonlinear regression models (2020)
  2. Kunihama, Tsuyoshi; Li, Zehang Richard; Clark, Samuel J.; Mccormick, Tyler H.: Bayesian factor models for probabilistic cause of death assessment with verbal autopsies (2020)
  3. Kurita, Takamitsu: Likelihood-based tests for parameter constancy in (I(2)) CVAR models with an application to fixed-term deposit data (2020)
  4. Li, Mengheng; Koopman, Siem Jan; Lit, Rutger; Petrova, Desislava: Long-term forecasting of El Niño events via dynamic factor simulations (2020)
  5. Alizadeh, Morad; Afshari, Mahmoud; Altun, Emrah; Ozel, Gamze; Eftekharian, Abbas: A new odd log-logistic Lindley distribution with properties and applications (2019)
  6. Cribari-Neto, Francisco; Pereira, Inara F. S.: Testing inference in heteroskedastic linear regressions: a comparison of two alternative approaches (2019)
  7. Hashimoto, Elizabeth M.; Ortega, Edwin M. M.; Cordeiro, Gauss M.; Cancho, Vicente G.; Klauberg, Carine: Zero-spiked regression models generated by gamma random variables with application in the resin oil production (2019)
  8. Irie, Kaoru; West, Mike: Bayesian emulation for multi-step optimization in decision problems (2019)
  9. João Duarte; Vinícius Mayrink: slfm: An R Package to Evaluate Coherent Patterns in Microarray Data via Factor Analysis (2019) not zbMATH
  10. Kobayashi, Genya; Kakamu, Kazuhiko: Approximate Bayesian computation for Lorenz curves from grouped data (2019)
  11. Lemonte, Artur J.; Moreno-Arenas, Germán: On residuals in generalized Johnson (S_B) regressions (2019)
  12. 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)
  13. Roume, Clément; Ezzina, Samar; Blain, Hubert; Delignières, Didier: Biases in the simulation and analysis of fractal processes (2019)
  14. Alizadeh, Morad; Altun, Emrah; Cordeiro, Gauss M.; Rasekhi, Mahdi: The odd power Cauchy family of distributions: properties, regression models and applications (2018)
  15. 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)
  16. Darolles, Serge; Francq, Christian; Laurent, Sébastien: Asymptotics of Cholesky GARCH models and time-varying conditional betas (2018)
  17. Dey, Sanku; Mazucheli, Josmar; Nadarajah, Saralees: Kumaraswamy distribution: different methods of estimation (2018)
  18. Doornik, Jurgen A.: Accelerated estimation of switching algorithms: the cointegrated VAR model and other applications (2018)
  19. Fattore, Marco; Pelagatti, Matteo; Vittadini, Giorgio: A least squares approach to latent variables extraction in formative-reflective models (2018)
  20. Felix Pretis; J. Reade; Genaro Sucarrat: Automated General-to-Specific (GETS) Regression Modeling and Indicator Saturation for Outliers and Structural Breaks (2018) not zbMATH

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