sgd: Stochastic Gradient Descent for Scalable Estimation (original) (raw)
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 |
| 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:
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