SAS/IML

The SAS® System provides a powerful and flexible programming language in a dynamic, interactive environment for programmers, statisticians, and researchers. Use the SAS System for data manipulation and general statistical analysis, then employ SAS/IML software’s interactive matrix language for more specific analysis and exploration. The fundamental data element in SAS/IML is the matrix, a two-dimensional (row-by-column) array of numeric or character values. You do not need to declare, dimension, or allocate storage for a data matrix because SAS/IML software does this automatically. You can change the dimension or type of a matrix, reset options, or replace modules at any time. You can open multiple files or access many libraries. Built-in matrix operators, functions, and subroutines can be applied to perform complex tasks such as matrix inversion or eigenvector generation.


References in zbMATH (referenced in 44 articles )

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  1. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  2. Islam, M. Ataharul; Chowdhury, Rafiqul I.: Analysis of repeated measures data (2017)
  3. Danjie Zhang; Ming-Hui Chen; Joseph Ibrahim; Mark Boye; Wei Shen: JMFit: A SAS Macro for Joint Models of Longitudinal and Survival Data (2016)
  4. Atsushi Kawaguchi; Gary Koch: sanon: An R Package for Stratified Analysis with Nonparametric Covariable Adjustment (2015)
  5. Pierre Bunouf; Geert Molenberghs; Jean-Marie Grouin; Herbert Thijs: A SAS Program Combining R Functionalities to Implement Pattern-Mixture Models (2015)
  6. Roger Bivand; Gianfranco Piras: Comparing Implementations of Estimation Methods for Spatial Econometrics (2015)
  7. El Hefnawy, Ali; Farag, Aya: A combined nonlinear programming model and Kibria method for choosing ridge parameter regression (2014)
  8. Hamed, Ramadan; El Hefnawy, Ali; Farag, Aya: Selection of the ridge parameter using mathematical programming (2013)
  9. Lesch, Scott M.; Jeske, Daniel R.: A new exponential GOF test for data subject to multiply type II censoring (2013)
  10. Lyles, Robert H.; Kupper, Lawrence L.: Approximate and pseudo-likelihood analysis for logistic regression using external validation data to model log exposure (2013)
  11. Xu, Shizhong: Principles of statistical genomics (2013)
  12. Zhao, Zheng; Zhang, Ruiwen; Cox, James; Duling, David; Sarle, Warren: Massively parallel feature selection: an approach based on variance preservation (2013)
  13. Sajobi, Tolulope T.; Lix, Lisa M.; Dansu, Bolanle M.; Laverty, William; Li, Longhai: Robust descriptive discriminant analysis for repeated measures data (2012)
  14. Buonaccorsi, John P.: Measurement error. Models, methods and applications (2010)
  15. Oja, Hannu: Multivariate nonparametric methods with R. An approach based on spatial signs and ranks. (2010)
  16. Perrett, Jamis J.: A SAS/IML companion for linear models. (2010)
  17. Son, Chang-Kyoon; Hong, Ki-Hak; Lee, Gi-Sung; Kim, Jong-Min: The calibration for two-phase randomized response estimator (2010)
  18. Son, Chang-Kyoon; Kim, Jong-Min; Hong, Ki-Hak; Lee, Gi-Sung: Calibration for randomized response estimators (2010)
  19. Wright, Stephen E.; Sigal, Belle M.; Bailer, A.John: Workweek optimization of experimental designs: exact designs for variable sampling costs (2010)
  20. Ajmani, Vivek B.: Applied econometrics using the SAS system (2009)

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