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:Weijie Su [aut], Yuancheng Zhu [aut, cre]

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'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1 exports 0.00 score 2 dependencies 7 scripts 143 downloads

Last updated 7 years agofrom:a4ca1712ef. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 16 2024
R-4.5-winOKSep 16 2024
R-4.5-linuxOKSep 16 2024
R-4.4-winOKSep 16 2024
R-4.4-macOKSep 16 2024
R-4.3-winOKSep 16 2024
R-4.3-macOKSep 16 2024

Exports:higrad

Dependencies:latticeMatrix