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 81 articles )

Showing results 1 to 20 of 81.
Sorted by year (citations)

1 2 3 4 5 next

  1. Tracie L. Shing, John S. Preisser, Richard C. Zink: GEECORR: A SAS macro for regression models of correlated binary responses and within-cluster correlation using generalized estimating equations (2020) arXiv
  2. Vila, Jean-Pierre; Gauchi, Jean-Pierre: Predictive control of discrete time stochastic nonlinear state space dynamical systems: a particle nonparametric approach (2019)
  3. Neville, Zachariah; Brownstein, Naomi C.: Macros to conduct tests of multimodality in SAS (2018)
  4. Seung Hyun; Weng Wong; Yarong Yang: VNM: An R Package for Finding Multiple-Objective Optimal Designs for the 4-Parameter Logistic Model (2018) not zbMATH
  5. Zachariah Neville, Naomi Brownstein: Macros to Conduct Tests of Multimodality in SAS (2018) arXiv
  6. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  7. Islam, M. Ataharul; Chowdhury, Rafiqul I.: Analysis of repeated measures data (2017)
  8. Shieh, Gwowen: The equivalence of two approaches to incorporating variance uncertainty in sample size calculations for linear statistical models (2017)
  9. Yeo, In-Kwon: An algorithm for computing the exact distribution of the Wilcoxon signed-rank statistic (2017)
  10. Danjie Zhang; Ming-Hui Chen; Joseph Ibrahim; Mark Boye; Wei Shen: JMFit: A SAS Macro for Joint Models of Longitudinal and Survival Data (2016) not zbMATH
  11. George Kalema, Geert Molenberghs: Generating Correlated and/or Overdispersed Count Data: A SAS Implementation (2016) not zbMATH
  12. Hong Wang, Xu Zhang: Confidence Band for the Differences between Two Direct Adjusted Survival Curves (2016) not zbMATH
  13. Atsushi Kawaguchi; Gary Koch: sanon: An R Package for Stratified Analysis with Nonparametric Covariable Adjustment (2015) not zbMATH
  14. Pierre Bunouf; Geert Molenberghs; Jean-Marie Grouin; Herbert Thijs: A SAS Program Combining R Functionalities to Implement Pattern-Mixture Models (2015) not zbMATH
  15. Roger Bivand; Gianfranco Piras: Comparing Implementations of Estimation Methods for Spatial Econometrics (2015) not zbMATH
  16. El Hefnawy, Ali; Farag, Aya: A combined nonlinear programming model and Kibria method for choosing ridge parameter regression (2014)
  17. Jorge González: SNSequate: Standard and Nonstandard Statistical Models and Methods for Test Equating (2014) not zbMATH
  18. Liang Xie and Laurence Madden: %HPGLIMMIX: A High-Performance SAS Macro for GLMM Estimation (2014) not zbMATH
  19. Masahiko Gosho: Criteria to Select a Working Correlation Structure in SAS (2014) not zbMATH
  20. Walter Schill; Dirk Enders; Karsten Drescher: A SAS Package for Logistic Two-Phase Studies (2014) not zbMATH

1 2 3 4 5 next