sgdnet is an R-package that fits elastic net-regularized generalized linear models to big data using the incremental gradient average algorithm SAGA (Defazio et al. 2014).
# install.packages("devtools") devtools::install_github("jolars/sgdnet")
It is simple to fit a model using sgdnet. The interface deliberately mimics that of glmnet to facilitate transitioning between the two.
First we load the package, and then we fit a multinomial model to the
iris data set. We
se the elastic net
0.8 using the
alpha argument to achieve a compromise between the
sgdnet fits the model across an automatically computed
regularization path. Altneratively, the user might supply their own path
library(sgdnet) fit <- sgdnet(iris[, 1:4], iris[, 5], family = "multinomial", alpha = 0.8) plot(fit)
sgdnet is open source software, licensed under GPL-3.
sgdnet uses semantic versioning.
The initial work on sgdnet was supported by Google through the Google Summer of Code program with Michael Weylandt and Toby Dylan Hocking as mentors.