Circadian rhythms are rhythms that oscillate about every 24 h, which has been observed in multiple physiological processes including core body temperature, hormone secretion, heart rate, blood pressure, and many others. Measuring circadian rhythm with wearables is based on a principle that there is increased movement during wake periods and reduced movement during sleep periods, and has been shown to be reliable and valid. This package can be used to extract nonparametric circadian metrics like intradaily variability (IV), interdaily stability (IS), and relative amplitude (RA); and parametric cosinor model and extended cosinor model coefficient. Details can be found in Junrui Di et al (2019) <doi:10.1007/s12561-019-09236-4>.

Designed for studies where animals tagged with acoustic tags are expected
    to move through receiver arrays. This package combines the advantages of automatic sorting and checking 
    of animal movements with the possibility for user intervention on tags that deviate from expected 
    behaviour. The three analysis functions (explore(), migration() and residency()) 
    allow the users to analyse their data in a systematic way, making it easy to compare results from 
    different studies.
    CJS calculations are based on Perry et al. (2012) <https://www.researchgate.net/publication/256443823_Using_mark-recapture_models_to_estimate_survival_from_telemetry_data>.

Recent studies haven shown that, on top of total daily active/sedentary volumes, the time 
  accumulation strategies provide more sensitive information. This package provides functions to extract 
  commonly used fragmentation metrics to quantify such time accumulation strategies based on minute level 
  actigraphy-measured activity counts data.

A tool to obtain activity counts, originally a translation of the 
  'python' package 'agcounts' <https://github.com/actigraph/agcounts>. This tool
  allows the processing of data from any accelerometer brand, with a more flexible 
  approach to handle different sampling frequencies.