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.

Version: 0.1.0
Imports: Matrix
Published: 2018-03-14
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

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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
OS X binaries: r-release: higrad_0.1.0.tgz, r-oldrel: higrad_0.1.0.tgz

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