VBTree: Vector Binary Tree to Make Your Data Management More Efficient

Vector binary tree provides a new data structure, to make your data visiting and management more efficient. If the data has structured column names, it can read these names and factorize them through specific split pattern, then build the mappings within double list, vector binary tree, array and tensor mutually, through which the batched data processing is achievable easily. The methods of array and tensor are also applicable. Detailed methods are described in Chen Zhang et al. (2020) <doi:10.35566/isdsa2019c8>.

Version: 0.1.1
Depends: R (≥ 2.10)
Imports: tensorA
Suggests: knitr, rmarkdown, testthat
Published: 2024-01-12
DOI: 10.32614/CRAN.package.VBTree
Author: Chen Zhang [aut, cre, cph] (0009-0007-7689-5030)
Maintainer: Chen Zhang <chen.zhang_06sept at foxmail.com>
BugReports: https://github.com/CubicZebra/VBTree/issues
License: GPL-3
URL: https://github.com/CubicZebra/VBTree
NeedsCompilation: no
Materials: README
CRAN checks: VBTree results


Reference manual: VBTree.pdf
Vignettes: intro


Package source: VBTree_0.1.1.tar.gz
Windows binaries: r-devel: VBTree_0.1.1.zip, r-release: VBTree_0.1.1.zip, r-oldrel: VBTree_0.1.1.zip
macOS binaries: r-release (arm64): VBTree_0.1.1.tgz, r-oldrel (arm64): VBTree_0.1.1.tgz, r-release (x86_64): VBTree_0.1.1.tgz, r-oldrel (x86_64): VBTree_0.1.1.tgz
Old sources: VBTree archive

Reverse dependencies:

Reverse imports: TPMplt


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