Description: 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>.

Description: 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>.
Imports: matlab, bitops, Rcpp (>= 0.12.10), tuneR

Title: Mixture Gaussian Graphical Models
Authors@R: c(person("Bochao", "Jia", role = c("aut", "ctb", "cre", "cph"), email = "jbc409@ufl.edu"),
person("Faming", "Liang", role = c("ctb"), email = "fmliang@purdue.edu"))
Description: 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.
Author: Bochao Jia [aut, ctb, cre, cph],

Authors@R: c(person("Bochao", "Jia", role = c("aut", "ctb", "cre", "cph"), email = "jbc409@gmail.com"),
Packaged: 2019-03-19 00:13:17 UTC; jiabochao

Title: Gaussian Graphical Models with Non-Convex Penalties
Description: Estimate Gaussian graphical models with non-convex penalties, including
seamless L0 Dicker, Huang, and Lin (2013) <doi:10.5705/ss.2011.074>,
smooth integration of counting and absolute deviation Lv and Fan (2009) <doi:10.1214/09-AOS683>,
logarithm Mazumder, Friedman, and Hastie (2011) <doi:10.1198/jasa.2011.tm09738>,
Lq, smoothly clipped absolute deviation Fan and Li (2001) <doi:10.1198/016214501753382273>,
and minimax concave penalty Zhang (2010) <doi:10.1214/09-AOS729>. There are also extensions
for computing variable inclusion probabilities and multiple regression coefficients.
glassoFast (>= 1.0), numDeriv (>= 2016.8-1.1), methods, psych