LISP-STAT

Lisp-Stat is an extensible statistical computing environment for data analysis, statistical instruction and research, with an emphasis on providing a framework for exploring the use of dynamic graphical methods. Extensibility is achieved by basing Lisp-Stat on the Lisp language, in particular on a subset of Common Lisp. Lisp-Stat extends standard Lisp arithmetic operations to perform element-wise operations on lists and vectors, and adds a variety of basic statistical and linear algebra functions. A portable window system interface forms the basis of a dynamic graphics system that is designed to work identically in a number of different graphical user interface environments, such as the Macintosh operating system, the X window system, and Microsoft Windows. A prototype-based object-oriented programming system is used to implement the graphics system and to allow it to be customized and adapted. The object-oriented programming system is also used as the basis for statistical model representations, such as linear and nonlinear regression models and generalized linear models. Many aspects of the system design were motivated by the S language.


References in zbMATH (referenced in 81 articles , 1 standard article )

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  1. Roos, Małgorzata; Martins, Thiago G.; Held, Leonhard; Rue, Håvard: Sensitivity analysis for Bayesian hierarchical models (2015)
  2. Wickham, Hadley: Mutable objects in R (2011)
  3. Wheeler, David C.; Hickson, Demarc A.; Waller, Lance A.: Assessing local model adequacy in Bayesian hierarchical models using the partitioned deviance information criterion (2010)
  4. Seo, Han Son: A visual procedure for optimal response transformations and curvature specifications (2009)
  5. Antoch, Jaromír: Environment for statistical computing (2008)
  6. Khuri, André I.; Mukherjee, Bhramar; Sinha, Bikas K.; Ghosh, Malay: Design issues for generalized linear models: a review (2006)
  7. Young, Forrest W.; Valero-Mora, Pedro M.; Friendly, Michael: Visual statistics: seeing data with dynamic interactive graphics. (2006)
  8. Weiss, Robert E.: Modeling longitudinal data. (2005)
  9. Yin, Xiangrong; Cook, R.Dennis: Direction estimation in single-index regressions (2005)
  10. Bura, E.: Using linear smoothers to assess the structural dimension of regressions (2003)
  11. Bura, Efstathia; Cook, R.Dennis: Rank estimation in reduced-rank regression (2003)
  12. Højsgaard, Søren: Split models for contingency tables (2003)
  13. Lambert, Diane: What use is statistics for massive data (2003)
  14. Reid, N.: Asymptotics and the theory of inference (2003)
  15. Valero-Mora, Pedro M.; Young, Forrest W.; Friendly, Michael: Visualizing categorical data in vista (2003)
  16. Cabrera, Javier; McDougall, Andrew: Statistical consulting (2002)
  17. Chen, Chun-Houh: Generalized association plots: Information visualization via iteratively generated correlation matrices (2002)
  18. Clyde, Merlise; Chaloner, Kathryn: Constrained design strategies for improving normal approximations in nonlinear regression problems (2002)
  19. Hall, Peter; Heckman, Nancy E.: Estimating and depicting the structure of a distribution of random functions (2002)
  20. Huh, Moon Yul; Kim, Kiyeol: Visualization of multidimensional data using modifications of the Grand Tour (2002)

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