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Introducing the BGLIMM Procedure for Bayesian Generalized Linear Mixed Models

Amy Shi introduces PROC BGLIMM (Bayesian generalized linear mixed models). This procedure is designed for fitting generalized linear mixed models. It enables you to model missing data, nested or nonnested multilevel models and repeated-measures data.

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12:06
Tools for Assessing a Propensity Score Model in SAS/STAT
Tools for Assessing a Propensity Score Model in SAS/STAT
12:16
Introducing the LASSO and Elastic Net Selection Methods in the REGSELECT Procedure
Introducing the LASSO and Elastic Net Selection Methods in the REGSELECT Procedure
10:05
Building and Using DAGs for Causal Inference
Building and Using DAGs for Causal Inference
11:10
What’s New in SAS/STAT 15.1
What’s New in SAS/STAT 15.1
6:17
Scoring Predictive Models with SAS/STAT Software
Scoring Predictive Models with SAS/STAT Software
11:05
Introducing the BGLIMM Procedure for Bayesian Generalized Linear Mixed Models
Introducing the BGLIMM Procedure for Bayesian Generalized Linear Mixed Models
10:47
Performing Restricted Mean Survival Time Analysis Using SAS/STAT
Performing Restricted Mean Survival Time Analysis Using SAS/STAT
8:42
Performing Cause-Specific Analysis of Competing Risks Using the PHREG Procedure
Performing Cause-Specific Analysis of Competing Risks Using the PHREG Procedure
13:18
Hierarchical Nonlinear Mixed Models using PROC NLMIXED
Hierarchical Nonlinear Mixed Models using PROC NLMIXED
10:26
Introducing the CAUSALMED Procedure for Causal Mediation Analysis
Introducing the CAUSALMED Procedure for Causal Mediation Analysis
8:02
Three Things You Should Know about Quantile Regression
Three Things You Should Know about Quantile Regression
13:01
What’s New in SAS/STAT 14.2
What’s New in SAS/STAT 14.2
7:19
Nonparametric Regression Modeling with the ADAPTIVEREG Procedure
Nonparametric Regression Modeling with the ADAPTIVEREG Procedure
5:59
An Introduction to the HPSPLIT Procedure for Building Classification and Regression Trees
An Introduction to the HPSPLIT Procedure for Building Classification and Regression Trees
9:05
Selecting Effects for Generalized Linear Models with the HPGENSELECT Procedure
Selecting Effects for Generalized Linear Models with the HPGENSELECT Procedure

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