Package: sgd 1.1.2

Junhyung Lyle Kim

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.

Authors:Junhyung Lyle Kim [cre, aut], Dustin Tran [aut], Panos Toulis [aut], Tian Lian [ctb], Ye Kuang [ctb], Edoardo Airoldi [ctb]

sgd_1.1.2.tar.gz
sgd_1.1.2.tar.gz(r-4.5-noble)sgd_1.1.2.tar.gz(r-4.4-noble)
sgd_1.1.2.tgz(r-4.4-emscripten)sgd_1.1.2.tgz(r-4.3-emscripten)
sgd.pdf |sgd.html✨
sgd/json (API)

# Install 'sgd' in R:
install.packages('sgd', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/airoldilab/sgd/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • winequality - Wine quality data of white wine samples from Portugal

3.87 score 74 scripts 295 downloads 2 mentions 2 exports 34 dependencies

Last updated 10 months agofrom:49a5569502. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-linux-x86_64OKOct 31 2024

Exports:predict_allsgd

Dependencies:BHbigmemorybigmemory.sriclicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangscalestibbleutf8uuidvctrsviridisLitewithr

Stochastic gradient decent methods for estimation with large data sets

Rendered fromsgd-jss.pdf.asisusingR.rsp::asison Oct 31 2024.

Last update: 2015-09-23
Started: 2015-09-23