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SAS has long developed software for data analysis, econometrics, operations research, and quality improvement. The purpose of these pages is to provide our users with technical information about using this software, including details about software capabilities, examples, papers, e-newsletter, and communities.

Featured News

Technical Support Tip Combining the Results from PROC SURVEYPHREG in PROC MIANALYZE

When there are CLASS variables in the model, the ODS table ParameterEstimates produced by PROC SURVEYPHREG does not match any of the available CLASSVAR= formats that PROC MIANALYZE requires. However, with minimal code in a DATA step, the data set can be converted to a format that PROC MIANALYZE can readily use.

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Highlights of the SAS/STAT 15.1 Release

This new release really does have something for everyone. Right, we say that all the time, but . . . The BGLIMM procedure provides full Bayesian inference for generalized linear mixed models, using the mixed model syntax you already know. The CAUSALGRAPH procedure extends our offerings for causal inference to include examining the structure of graphical causal models. And the RMSTREG procedure analyzes time-to-event data by using regression with response to the restricted mean survival time (RMST). You can also fit the semiparametric proportional hazards model to interval-censored data (ICPHREG), perform Bayesian analysis of the proportional hazards spline mode, and do counterfactual analysis using quantile regression.

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Optimization Modeling with Python and SAS Viya

Python has become a popular programming language for both data analytics and mathematical optimization. With SAS Viya and its Python interface, Python programmers can use the state-of-the-art optimization solvers that SAS provides. This paper demonstrates an approach for Python programmers to naturally model their optimization problems, solve them by using SAS Optimization solver actions, and view and interact with the results.

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blog Distinguished Research Statistician Developer Rick Wicklin shows you how to resample residuals to obtain bootstrap regression estimates.

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