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-4.7-any)higrad_0.1.0.zip(r-4.6-any)higrad_0.1.0.zip(r-4.5-any)
higrad_0.1.0.tgz(r-4.6-any)higrad_0.1.0.tgz(r-4.5-any)
higrad_0.1.0.tar.gz(r-4.7-any)higrad_0.1.0.tar.gz(r-4.6-any)
higrad_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
higrad/json (API)
| # 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 from:a4ca1712ef. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 85 | ||
| source / vignettes | OK | 156 | ||
| linux-release | OK | 81 | ||
| macos-release | OK | 134 | ||
| macos-oldrel | OK | 166 | ||
| windows-devel | OK | 66 | ||
| windows-release | OK | 63 | ||
| windows-oldrel | OK | 68 | ||
| wasm-release | OK | 88 |
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 |