Correlation bounds: R and Matlab code for computing bounds and for simulation of many continuous, discrete and mixed-type bivariate distribuitions. By Bahjat Qaqish and Sergei Leonov. [Leonov, S. & Qaqish, B. Stat Papers (2017). https://doi.org/10.1007/s00362-017-0960-2]

Lossless Data Reduction for Clustering, Discriminant Analysis and Similar Methods: R, Matlab and SAS code based on: Qaqish, B. F., O'Brien, J. J., Hibbard, J. C., Clowers, K. J. (2017). Accelerating high-dimensional clustering with lossless data reduction. BIOINFORMATICS, Volume: 33, Issue: 18, Pages: 2867-2872. DOI: 10.1093/bioinformatics/btx328

The Pool Adjacent Violators (PAVA) Algorithm: an R function written in C. By Bahjat Qaqish (2003).

R Extensions in C: A two-page introduction to writing R extensions in C. Includes a complete example. By Bahjat Qaqish (2003).

An R function for Variance Components for Nested Binary Responses with Three Levels of Nesting: (Example: Clinics/Physicians/Patients). By Bahjat Qaqish (2005).

A Matrix Library in C. By Bahjat Qaqish (2003).

Software for profiling CPU and memory performance: Source code in C and Windowns executables. By Bahjat Qaqish (2005).

Orthogonalized Residuals: SAS macro for ORTH estimation of regression models for multivariate Bernoulli outcomes, with models for the marginal means and pairwise log odds ratios. Estimation is by the method of Orthogonalized Residuals developed by the authors. By Richard Zink and Bahjat Qaqish.

Orthogonalized Residuals with model-based Lambda: SAS macro for ORTH estimation of regression models for multivariate Bernoulli outcomes, with models for the marginal means and pairwise log odds ratios. Allows various model-based choices for lambda, including lambda=0 (alternating logisitc regression, ALR). Estimation is by the method of Orthogonalized Residuals developed by the authors. By Richard Zink and Bahjat Qaqish.

Orthogonalized Residuals Regression Diagnostics: SAS macro for ORTH estimation and computation of regression diagnostics. By Kunthel By, John Preisser, Jamie Perin, Richard Zink and Bahjat Qaqish.

Orthogonalized Residuals Efficieny Calculations: Code for computing and plotting the asymptotic efficieny of ORTH, ALR and various other procedures, relative to GEE2 under various models for third and fourth moments. By Bahjat Qaqish.

Continuous Toxicity Monitoring in Phase II Trials in Oncology: DOS/Windows programs and sample input and output files for implementing the methods described in Ivanova, Qaqish & Schell (2005 Biometrics 61, 540-545)

The Conditional Linear Family of Multivariate Bernoulli Distributions, Computation and Simulation: SAS/IML modules, based on Qaqish (2003, Biometrika 90, 455-63). By Bahjat Qaqish (2003).

GEE1 Regression Diagnostics: A SAS macro based on Preisser & Qaqish (1996, Biometrika 83, 551-62). By John Preisser (2003).

BLEX: GEE1 SAS macro for binary data, logit link, exchangeable correlation; optimized for LARGE cluster sizes. BLEX is short for Binary Logit EXchangeable. By Bahjat Qaqish and Habib Moalem (1994).

Bivariate Logistic Regression: SAS/IML program for maximum-likelihood estimation (two marginal logits and a log odds ratio). By Bahjat Qaqish (2000).

GEE2: DOS version, IBM VM/CMS version, DOS/Windows version: Pascal source (DOS, VM/CMS), Fortran source (DOS/Windows), executable and examples. The Fortran port of the original Pascal code was developed by a project of the Population Council funded by the University of North Carolina Evaluation Project (contract number 5-35676). The Evaluation Project was funded by the United States Agency for International Development (contract number DPE-3060-C-00-1054-1). By Bahjat Qaqish (1989).

A SAS macro for Variance Components for Nested Binary Responses with Three Levels of Nesting: (Example: patients within physicians and physicians within medical practices). By Bahjat Qaqish and Habib Moalem (1993).

Extra-binomial variation (Overdispersion): Sample SAS programs. By Bahjat Qaqish (2003).