Joint analysis and imputation of incomplete data in the Bayesian
framework, using (generalized) linear (mixed) models and extensions there of,
survival models, or joint models for longitudinal and survival data, as
described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>.
Incomplete covariates, if present, are automatically imputed.
The package performs some preprocessing of the data and creates a 'JAGS'
model, which will then automatically be passed to 'JAGS'
<https://mcmc-jags.sourceforge.io/> with the help of
the package 'rjags'.

rjags, mcmcse, coda, rlang, future, mathjaxr, survival, MASS
knitr, rmarkdown, bookdown, foreign, ggplot2, ggpubr,
testthat, covr

Mathieu Ribatet <ribatet@lyon.cemagref.fr> and Bertrand Iooss
Some functions usefull to perform joint modelling of
Mean and Dispersion through two interlinked GLM's

Joint Modelling of Mean and Dispersion (outdated, see package JointModeling)
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package is outdated.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.

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

Some functions usefull to perform joint modelling of Mean
and Dispersion through two interlinked GLM's or GAM's.

Mathieu Ribatet <mathieu.ribatet@epfl.ch> and Bertrand Iooss
Some functions useful to perform joint modelling of Mean
and Dispersion through two interlinked GLM's or GAM's.

Sparse Gaussian Graphical Model Estimation, Visualization and
Evaluation
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"))
Beilun Wang [aut],
Yanjun Qi [aut],
Zhaoyang Wang [aut, cre]
R (>= 3.0.0), lpSolve, pcaPP, igraph, parallel
MASS, brainR, misc3d, oro.nifti, shiny, rgl, methods
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).