sgd: Stochastic Gradient Descent for Scalable Estimation

A fast and flexible set of tools for large scale estimation. It features many stochastic gradient methods, built-in models, visualization tools, automated hyperparameter tuning, model checking, interval estimation, and convergence diagnostics.

Version: 1.1.1
Imports: ggplot2, MASS, methods, Rcpp (≥ 0.11.3), stats
LinkingTo: BH, bigmemory, Rcpp, RcppArmadillo
Suggests: bigmemory, glmnet, gridExtra, R.rsp, testthat
Published: 2019-07-12
Author: Junhyung Lyle Kim [cre, aut], Dustin Tran [aut], Panos Toulis [aut], Tian Lian [ctb], Ye Kuang [ctb], Edoardo Airoldi [ctb]
Maintainer: Junhyung Lyle Kim <lylejkim at gmail.com>
BugReports: https://github.com/airoldilab/sgd/issues
License: GPL-2
URL: https://github.com/airoldilab/sgd
NeedsCompilation: yes
Materials: README
CRAN checks: sgd results

Downloads:

Reference manual: sgd.pdf
Vignettes: Stochastic gradient decent methods for estimation with large data sets
Package source: sgd_1.1.1.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: sgd_1.1.1.zip
OS X binaries: r-release: sgd_1.1.1.tgz, r-oldrel: sgd_1.1.1.tgz
Old sources: sgd archive

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