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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: | 1.1.2 |
Imports: | ggplot2, MASS, methods, Rcpp (≥ 0.11.3), stats |
LinkingTo: | BH, bigmemory, Rcpp, RcppArmadillo |
Suggests: | bigmemory, glmnet, gridExtra, R.rsp, testthat |
Published: | 2024-01-31 |
DOI: | 10.32614/CRAN.package.sgd |
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 <jlylekim 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 |
Reference manual: | sgd.pdf |
Vignettes: |
Stochastic gradient decent methods for estimation with large data sets |
Package source: | sgd_1.1.2.tar.gz |
Windows binaries: | r-devel: sgd_1.1.2.zip, r-release: sgd_1.1.2.zip, r-oldrel: sgd_1.1.2.zip |
macOS binaries: | r-release (arm64): sgd_1.1.2.tgz, r-oldrel (arm64): sgd_1.1.2.tgz, r-release (x86_64): sgd_1.1.2.tgz, r-oldrel (x86_64): sgd_1.1.2.tgz |
Old sources: | sgd archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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