PML: Penalized Multi-Band Learning for Circadian Rhythm Analysis Using Actigraphy

Penalized Multi-Band Learning algorithm can be effectively implemented for circadian rhythm analysis and daily activity pattern characterization using actigraphy (continuously measured objective physical activity data). Functions for interactive visualization of actigraph data are also included. Method reference: Li, X., Kane, M., Zhang, Y., Sun, W., Song, Y., Dong, S., Lin, Q., Zhu, Q., Jiang, F., Zhao, H. (2019) A Novel Penalized Multi-band Learning Approach Characterizes the Consolidation of Sleep-Wake Circadian Rhythms During Early Childhood Development.

Version: 1.1
Depends: R (≥ 3.4.0)
Imports: trelliscopejs, tidyr, rbokeh, dplyr, tibble
Suggests: knitr, rmarkdown
Published: 2019-09-17
Author: Xinyue Li [aut, cre], Michael Kane [aut]
Maintainer: Xinyue Li <xinyue.li at yale.edu>
BugReports: https://github.com/xinyue-L/PML/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/xinyue-L/PML
NeedsCompilation: no
CRAN checks: PML results

Downloads:

Reference manual: PML.pdf
Vignettes: Package PML User Manual
Package source: PML_1.1.tar.gz
Windows binaries: r-devel: PML_1.1.zip, r-release: PML_1.1.zip, r-oldrel: PML_1.1.zip
OS X binaries: r-release: PML_1.1.tgz, r-oldrel: PML_1.1.tgz
Old sources: PML archive

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