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

Documentation:

Reference manual:

higrad.pdf

Downloads:

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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