sgdnet

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).

Installation

sgdnet is not currently available on
CRAN but can be installed using the
devtools package:

# install.packages("devtools")
devtools::install_github("jolars/sgdnet")

Usage

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
penalty to
0.8 using the alpha argument to achieve a compromise between the
ridge and
lasso penalties.

sgdnet fits the model across an automatically computed
regularization path. Altneratively, the user might supply their own path
using the lambda argument.

library(sgdnet)
fit <- sgdnet(iris[, 1:4], iris[, 5], family = "multinomial", alpha = 0.8)
plot(fit)

License

sgdnet is open source software, licensed under GPL-3.

Versioning

sgdnet uses semantic versioning.

Acknowledgements

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.