testthat, covr, knitr, rmarkdown, actilifecounts

data.table, foreach, doParallel, signal, zoo, GENEAread,
tuneR, unisensR, ineq, read.gt3x, ActCR, methods, GGIRread

Wearable Accelerometer Data File Readers
c(person("Vincent T","van Hees",role=c("aut","cre"),
email="v.vanhees@accelting.com"),
person(given = "Patrick",family = "Bos",
role = c("aut"),
email = "egpbos@gmail.com",
comment = c(ORCID = "0000-0002-6033-960X")),
person("Jing Hua","Zhao",role="ctb"),
person("Evgeny","Mirkes",role="ctb"),
person("Dan","Jackson",role="ctb"),
person("Medical Research Council UK",  role = c("cph", "fnd")),
person("Accelting",  role = c("cph", "fnd")))
Reads data collected from wearable acceleratometers as used in sleep and physical activity research. Currently supports file formats: binary data from 'GENEActiv' <https://activinsights.com/>, binary data from GENEA devices (not for sale), and .cwa-format data from 'Axivity' <https://axivity.com>.
Vincent T van Hees [aut, cre],
Patrick Bos [aut] (<https://orcid.org/0000-0002-6033-960X>),
Jing Hua Zhao [ctb],
Evgeny Mirkes [ctb],
Dan Jackson [ctb],
Medical Research Council UK [cph, fnd],
Accelting [cph, fnd]

Reads data collected from wearable acceleratometers as used in sleep and physical activity research. Currently supports file formats: binary data from 'GENEActiv' <https://activinsights.com/>, binary data from GENEA devices (not for sale), and .cwa-format data from 'Axivity' <https://axivity.com>. Primarily designed to complement R package GGIR <https://CRAN.R-project.org/package=GGIR>.

Reads data collected from wearable acceleratometers as used in sleep and physical activity research. Currently supports file formats: binary data from 'GENEActiv' <https://activinsights.com/>, binary data from GENEA devices (not for sale), and .cwa-format and .wav-format data from 'Axivity' <https://axivity.com>. Primarily designed to complement R package GGIR <https://CRAN.R-project.org/package=GGIR>.
matlab, bitops, Rcpp (>= 0.12.10), tuneR

c(person("Bochao", "Jia", role = c("aut", "ctb", "cre", "cph"), email = "jbc409@ufl.edu"),
person("Faming", "Liang", role = c("ctb"), email = "fmliang@purdue.edu"))
The Gaussian graphical model is a widely used tool for learning gene regulatory networks with high-dimensional gene expression data. For many real problems, the data are heterogeneous, which may contain some subgroups or come from different resources. This package provide a Gaussian Graphical Mixture Model (GGMM) for the heterogeneous data. You can refer to Jia, B. and Liang, F. (2018) at <arXiv:1805.02547> for detail.
Bochao Jia [aut, ctb, cre, cph],
Faming Liang [ctb]