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) <doi:10.48550/arXiv.1802.04876> for details.

Version: 0.1.0
Imports: Matrix
Published: 2018-03-14
DOI: 10.32614/CRAN.package.higrad
Author: Weijie Su [aut], Yuancheng Zhu [aut, cre]
Maintainer: Yuancheng Zhu <yuancheng.zhu at gmail.com>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
CRAN checks: higrad results


Reference manual: higrad.pdf


Package source: higrad_0.1.0.tar.gz
Windows binaries: r-devel: higrad_0.1.0.zip, r-release: higrad_0.1.0.zip, r-oldrel: higrad_0.1.0.zip
macOS binaries: r-release (arm64): higrad_0.1.0.tgz, r-oldrel (arm64): higrad_0.1.0.tgz, r-release (x86_64): higrad_0.1.0.tgz, r-oldrel (x86_64): higrad_0.1.0.tgz


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