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 62 articles )

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  1. Seung Hyun; Weng Wong; Yarong Yang: VNM: An R Package for Finding Multiple-Objective Optimal Designs for the 4-Parameter Logistic Model (2018)
  2. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  3. Islam, M. Ataharul; Chowdhury, Rafiqul I.: Analysis of repeated measures data (2017)
  4. Yeo, In-Kwon: An algorithm for computing the exact distribution of the Wilcoxon signed-rank statistic (2017)
  5. Danjie Zhang; Ming-Hui Chen; Joseph Ibrahim; Mark Boye; Wei Shen: JMFit: A SAS Macro for Joint Models of Longitudinal and Survival Data (2016)
  6. Atsushi Kawaguchi; Gary Koch: sanon: An R Package for Stratified Analysis with Nonparametric Covariable Adjustment (2015)
  7. Pierre Bunouf; Geert Molenberghs; Jean-Marie Grouin; Herbert Thijs: A SAS Program Combining R Functionalities to Implement Pattern-Mixture Models (2015)
  8. Roger Bivand; Gianfranco Piras: Comparing Implementations of Estimation Methods for Spatial Econometrics (2015)
  9. El Hefnawy, Ali; Farag, Aya: A combined nonlinear programming model and Kibria method for choosing ridge parameter regression (2014)
  10. Jorge González: SNSequate: Standard and Nonstandard Statistical Models and Methods for Test Equating (2014)
  11. Liang Xie and Laurence Madden: %HPGLIMMIX: A High-Performance SAS Macro for GLMM Estimation (2014)
  12. Walter Schill; Dirk Enders; Karsten Drescher: A SAS Package for Logistic Two-Phase Studies (2014)
  13. Hamed, Ramadan; El Hefnawy, Ali; Farag, Aya: Selection of the ridge parameter using mathematical programming (2013)
  14. Lesch, Scott M.; Jeske, Daniel R.: A new exponential GOF test for data subject to multiply type II censoring (2013)
  15. Lyles, Robert H.; Kupper, Lawrence L.: Approximate and pseudo-likelihood analysis for logistic regression using external validation data to model log exposure (2013)
  16. Roberto Fontana; Sabrina Sampò: Minimum-Size Mixed-Level Orthogonal Fractional Factorial Designs Generation: A SAS-Based Algorithm (2013)
  17. Sarah Kreidler; Keith Muller; Gary Grunwald; Brandy Ringham; Zacchary Coker-Dukowitz; Uttara Sakhadeo; Anna Barón; Deborah Glueck: GLIMMPSE: Online Power Computation for Linear Models with and without a Baseline Covariate (2013)
  18. Xu, Shizhong: Principles of statistical genomics (2013)
  19. Zhao, Zheng; Zhang, Ruiwen; Cox, James; Duling, David; Sarle, Warren: Massively parallel feature selection: an approach based on variance preservation (2013)
  20. Richard Zink; Gary Koch: NParCov3: A SAS/IML Macro for Nonparametric Randomization-Based Analysis of Covariance (2012)

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