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

Documentation:

Reference manual:	sgd.pdf
Vignettes:	Stochastic gradient decent methods for estimation with large data sets

Downloads:

Package source:	sgd_1.1.1.tar.gz
Windows binaries:	r-devel: sgd_1.1.1.zip, r-release: sgd_1.1.1.zip, r-oldrel: sgd_1.1.1.zip
macOS binaries:	r-release (arm64): sgd_1.1.1.tgz, r-oldrel (arm64): sgd_1.1.1.tgz, r-release (x86_64): sgd_1.1.1.tgz, r-oldrel (x86_64): sgd_1.1.1.tgz
Old sources:	sgd archive

Reverse dependencies:

Reverse imports:

mombf

Linking:

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