There is not yet any way in R to fully leverage the power of stochastic gradient algorithms for fitting generalized linear models (GLM). The overarching goal of this project is to develop the R package sgdnet into a mature state for the implementation of the efficient SAGA algorithm for elastic net-penalized GLMs, targeting the big data setting where observations greatly outnumber variables. It will result in a CRAN submission.

Student

Qincheng Lu

Mentors

  • Toby Hocking
  • Michael Weylandt
  • Johan Larsson
close

2019