Imports: rjags, mcmcse, coda, rlang, future, mathjaxr, survival, MASS

Title: Joint Modelling of Mean and Dispersion
Author: Mathieu Ribatet <ribatet@lyon.cemagref.fr> and Bertrand Iooss
Description: Some functions usefull to perform joint modelling of
Mean and Dispersion through two interlinked GLM's
Packaged: Thu Nov 24 19:25:52 2005; mathieu

Packaged: Thu Apr  6 16:36:00 2006; iooss

Packaged: Fri Sep 22 14:17:55 2006; ribatema

Package: JointGLM
Version: 1.0-3
Date: 2006-11-02
Title: Joint Modelling of Mean and Dispersion (outdated, see package JointModeling)
Author: Mathieu Ribatet <ribatet@lyon.cemagref.fr> and Bertrand Iooss
Maintainer: Bertrand Iooss <bertrand.iooss@cea.fr>
Depends: R (>= 1.8.0), mgcv
Description: Some functions usefull to perform joint modelling of
Mean and Dispersion through two interlinked GLM's. This
package is outdated. We recommend the use of the
JointModeling package which generalizes this one.
License: GPL version 2 or newer
Packaged: Thu Nov  2 11:29:46 2006; ribatema

Title: Semiparametric Joint Models for Longitudinal and Counting
Description: Joint fit of a semiparametric regression model for longitudinal responses and a semiparametric transformation model for time-to-event data.
Depends: R (>= 3.2.0), lme4, survival, splines, statmod

Description: Some functions usefull to perform joint modelling of Mean
and Dispersion through two interlinked GLM's or GAM's.
Packaged: Thu Nov  2 11:29:53 2006; ribatema

Author: Mathieu Ribatet <mathieu.ribatet@epfl.ch> and Bertrand Iooss
Description: Some functions useful to perform joint modelling of Mean

Title: Sparse Gaussian Graphical Model Estimation, Visualization and
Authors@R: c(person("Beilun", "Wang", role = c("aut"), email = "bw4mw@virginia.edu"), person("Yanjun", "Qi", role = "aut", email = "yanjun@virginia.edu"),person("Zhaoyang", "Wang", role = c("aut","cre"), email = "zw4dn@virginia.edu"))
Depends: R (>= 3.0.0), lpSolve, pcaPP, igraph, parallel
Imports: MASS, brainR, misc3d, oro.nifti, shiny, rgl, methods
Description: A set of tools for performing sparse Gaussian graphical model (joint, multiple and difference) estimation from high dimensional dataset. It contains a general purpose visualization function as well as a specialized function for 3d brain network. Simulation and evaluation modules are available. It also contains a simple GUI built in shiny for easy graph visualization. Methods include SIMULE (Wang B et al. (2017) <doi:10.1007/s10994-017-5635-7>), WSIMULE (Singh C et al. (2017) <arXiv:1709.04090v2>), DIFFEE (Wang B et al. (2018) <arXiv:1710.11223>), FASJEM (Wang B et al. (2018) <arXiv:1702.02715v3>), JEEK (Wang B et al. (2018) <arXiv:1806.00548>) and DIFFEEK (Wang B et al, under final review for publication).