ind_to_day

<p>covariates data for individuals to be merged.</p>

<p>Take individual characteristics and map it to individual-day observations. For example, individual A/B is 40/45 
years old and has 4/2 day observations in dataset D. Therefore, mapping age to dataset D generates 
40, 40, 40, 40, 45, 45 for day 1,2,3,4/1,2 observation of A/B respectively.</p>

internal

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.

Xinyue Li

ind_to_day: trelliscope auxillary function

Description

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