Rcpp (>= 0.12.18), hetGP (>= 1.1.1), lhs, numDeriv, methods,
MASS, RcppProgress

Provides functions to fit kernel density functions
to animal activity time data; plot activity distributions;
quantify overall levels of activity; statistically compare
activity metrics through bootstrapping; and evaluate variation
in linear variables with time (or other circular variables).

Provides functions to express clock time data relative to
anchor points (typically solar); fit kernel density functions to animal
activity time data; plot activity distributions; quantify overall
levels of activity; statistically compare activity metrics through
bootstrapping; evaluate variation in linear variables with time (or
other circular variables).

c(
person("Ruben", "Brondeel", role = "aut"),
person("Javad", "Rahimipour Anaraki", role = "aut"),
person("SeyedJavad", "KhataeiPour", role = c("aut", "cre"), email = "skhataeipour@mun.ca"),
person("Daniel", "Fuller", role = c("aut", "cph")),
person(family = "Beap Lab", role = "cph")
)
ActiLife software generates activity counts from data collected by Actigraph accelerometers <https://s3.amazonaws.com/actigraphcorp.com/wp-content/uploads/2017/11/26205758/ActiGraph-White-Paper_What-is-a-Count_.pdf>.
Actigraph is one of the most common research-grade accelerometers. There is considerable research
validating and developing algorithms for human activity using ActiLife counts. Unfortunately,
ActiLife counts are proprietary and difficult to implement if researchers use different accelerometer brands.
The code  creates ActiLife counts from raw acceleration data for different accelerometer brands and it is developed
based on the study done by Brond and others (2017) <doi:10.1249/MSS.0000000000001344>.
seewave, signal, tibble, lubridate, magrittr
Ruben Brondeel [aut],
Javad Rahimipour Anaraki [aut],
SeyedJavad KhataeiPour [aut, cre],
Daniel Fuller [aut, cph],
Beap Lab [cph]

Circular Mixed Effect Mixture Models of Animal Activity Patterns
Bayesian parametric generalized circular mixed effect mixture models (GCMMs) for estimating animal activity patterns from camera trap data and other nested data structures using 'JAGS', including automatic Bayesian k-cluster selection and random circular intercepts for nested data. The GCMM function automatically selects the number of components for the mixture model (supporting up to 4 mixture components) based on a Bayesian linear finite normal mixture model and fits a Bayesian parametric circular mixed effect  mixture model with one or two random effects as random circular intercepts with a a von Mises or wrapped Cauchy distribution. Provides graphs of the combined mixture model or separate mixture components. Functionality is provided to allow quantitative  comparisons between model parameters. See Campbell et al. (in press) It's time to expand our analyses of animal activity;  Campbell et al. (in press) Temporal and microspatial niche partitioning; Campbell et al. (in press) A novel approach to comparing animal activity patterns. News, updates, and tutorials will be available on www.atlasgoldenwolf.org/stats and www.github.com/LizADCampbell .
mclust, runjags, circular, overlap, stats, graphics, utils,