Package: higrad 0.1.0
higrad: Statistical Inference for Online Learning and Stochastic Approximation via HiGrad
Implements the Hierarchical Incremental GRAdient Descent (HiGrad) algorithm, a first-order algorithm for finding the minimizer of a function in online learning just like stochastic gradient descent (SGD). In addition, this method attaches a confidence interval to assess the uncertainty of its predictions. See Su and Zhu (2018) <arxiv:1802.04876> for details.
Authors:
higrad_0.1.0.tar.gz
higrad_0.1.0.zip(r-4.5)higrad_0.1.0.zip(r-4.4)higrad_0.1.0.zip(r-4.3)
higrad_0.1.0.tgz(r-4.4-any)higrad_0.1.0.tgz(r-4.3-any)
higrad_0.1.0.tar.gz(r-4.5-noble)higrad_0.1.0.tar.gz(r-4.4-noble)
higrad_0.1.0.tgz(r-4.4-emscripten)higrad_0.1.0.tgz(r-4.3-emscripten)
higrad.pdf |higrad.html✨
higrad/json (API)
NEWS
| # Install 'higrad' in R: |
| install.packages('higrad', repos = c('https://captainyc.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 years agofrom:a4ca1712ef. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 15 2024 |
| R-4.5-win | OK | Nov 15 2024 |
| R-4.5-linux | OK | Nov 15 2024 |
| R-4.4-win | OK | Nov 15 2024 |
| R-4.4-mac | OK | Nov 15 2024 |
| R-4.3-win | OK | Nov 15 2024 |
| R-4.3-mac | OK | Nov 15 2024 |
Exports:higrad
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Fitting HiGrad | higrad |
| Plot a 'higrad' Object | plot.higrad |
| Obtain Prediction and Confidence Intervals From a HiGrad Fit | predict.higrad |
| Print a 'higrad' Object | print.higrad |
