spelling, knitr, rmarkdown, deSolve, testthat (>= 3.0.0),
covr

purrr, stringr, tidyr, ggplot2, lhs, MASS, R6, viridis,
mvtnorm, GGally, rlang, isoband, mclust, pdist

High-Dimensional Mixed Graphical Models Estimation
Provides weighted group lasso framework for high-dimensional mixed data graph estimation.
In the graph estimation stage, the graph structure is estimated by maximizing the conditional
likelihood of one variable given the rest. We focus on the conditional loglikelihood of each variable
and fit separate regressions to estimate the parameters, much in the spirit of the neighborhood
selection approach proposed by Meinshausen-Buhlmann for the Gaussian Graphical Model and by Ravikumar
for the Ising Model. Currently, the discrete variables can only take two values. In the future, method
for general discrete data and for visualizing the estimated graph will be added.
For more details, see the linked paper.
rgl, Matrix, glmnet, MASS, nat, binaryLogic, Rcpp, stats,
methods

Provides weighted lasso framework for high-dimensional mixed data graph estimation.
In the graph estimation stage, the graph structure is estimated by maximizing the conditional
likelihood of one variable given the rest. We focus on the conditional loglikelihood of each variable
and fit separate regressions to estimate the parameters, much in the spirit of the neighborhood
selection approach proposed by Meinshausen-Buhlmann for the Gaussian Graphical Model and by Ravikumar
for the Ising Model. Currently, the discrete variables can only take two values. In the future, method
for general discrete data and for visualizing the estimated graph will be added.
For more details, see the linked paper.

c(
person("Matthias", "Speidel", , "matthias.speidel@googlemail.com", role = c("aut", "cre"),
comment = "Institute for Employment Research, Nuremberg, Germany"),
person("Joerg", "Drechsler", role = "aut",
comment = "Institute for Employment Research, Nuremberg, Germany"),
person("Shahab", "Jolani", role = "aut",
comment = "Maastricht University, Maastricht, The Netherlands"))
Runs single level and multilevel imputation models. The user just has to pass the data to the main function and, optionally, his analysis model. Basically the package then translates this analysis model into commands to impute the data according to it with functions from 'mice', 'MCMCglmm' or routines build for this package.
Matthias Speidel [aut, cre] (Institute for Employment Research,
Nuremberg, Germany),
Joerg Drechsler [aut] (Institute for Employment Research, Nuremberg,
Germany),
Shahab Jolani [aut] (Maastricht University, Maastricht, The
Netherlands)