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.


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1.2 by Xinyue Li, a year ago

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Authors: Xinyue Li [aut, cre] , Michael Kane [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports tidyr, rbokeh, dplyr, tibble

Suggests knitr, rmarkdown

See at CRAN