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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 |
| 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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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