MetaCycle: Evaluate Periodicity in Large Scale Data

There are two functions-meta2d and meta3d for detecting rhythmic signals from time-series datasets. For analyzing time-series datasets without individual information, 'meta2d' is suggested, which could incorporates multiple methods from ARSER, JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms. For analyzing time-series datasets with individual information, 'meta3d' is suggested, which takes use of any one of these three methods to analyze time-series data individual by individual and gives out integrated values based on analysis result of each individual.

Version: 1.2.0
Depends: R (≥ 3.0.2)
Imports: gnm
Suggests: knitr, rmarkdown, parallel
Published: 2019-04-18
DOI: 10.32614/CRAN.package.MetaCycle
Author: Gang Wu [aut, cre], Ron Anafi [aut, ctb], John Hogenesch [aut, ctb], Michael Hughes [aut, ctb], Karl Kornacker [aut, ctb], Xavier Li [aut, ctb], Matthew Carlucci [aut, ctb]
Maintainer: Gang Wu <wggucas at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: MetaCycle results


Reference manual: MetaCycle.pdf
Vignettes: Introduction to MetaCycle


Package source: MetaCycle_1.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): MetaCycle_1.2.0.tgz, r-oldrel (arm64): MetaCycle_1.2.0.tgz, r-release (x86_64): MetaCycle_1.2.0.tgz, r-oldrel (x86_64): MetaCycle_1.2.0.tgz
Old sources: MetaCycle archive

Reverse dependencies:

Reverse imports: DiscoRhythm


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