Advanced Regressions and High-performance Computing

August 27, 2019



Dr. Olga, a professor at CSU Long Beach will give a talk and advanced regression models. It is based on her recently released book “Advanced Regression Models with SAS and R.” This will also be a great introduction to the short course that we are planning for September. Check out the book at

George G Vega Yon will be talking about doing high-performance computing in R. He will discuss the ‘parallel’ package for parallel computing, Rcpp integrating C++ R, RcppArmadillo which is a linear algebra library and OpenMP which is an API for multiprocessing applications.

Come early, network, enjoy the food and talks. Do not forget to purchase a raffle ticket or two. It helps support the meet-up and we have a great prize.


6:30 - 6:50 Networking 6:50 - 7:00 Welcome & general announcement 7:00 - 7:30 Regression Models for Count Data 7:30 - 8:00 A Brief Introduction to Using R for High-Performance Computing 8:00 - Raffle 8:00 - 8:30 Networking and Clean-up

Talk 1

Title: Regression Models for Count Data

Speaker: Olga Korosteleva


In my presentation, I plan to give a brief overview of various regression models: their settings, mathematical expressions, and implementation to datasets with complete R codes. The following models will be considered: linear (normal response), gamma (right-skewed response), logistic (binary response), Poisson, zero-truncated Poisson, zero-inflated Poisson, longitudinal model, and hierarchical model.

Talk 2

Title: A Brief Introduction to Using R for High-Performance Computing

Speaker: George G Vega Yon


While the R programming language was not developed for High-Performance Computing (think about the ‘for’ loops!), thanks to its always thriving community of users, there are several ways in which R can be used to perform HPC. In this presentation, I will give you a general overview of HPC in R with a particular focus on multi-core processing (i.e. no big data for now), and introduce some of the available tools for enhancing your R code. The presentation will include some examples using the ‘parallel’ package, Rcpp, and Rcpp(Armadillo) + OpenMP.

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