person("Katherine", "Hartmann", email = "klh64@cornell.edu", role = c("aut", "cre"))
Tools to download and organize large-scale, publicly available genomic studies on a candidate gene scale. Includes functions to integrate these data sources and compare features across candidate genes.
stringr, grDevices, graphics, stats, utils, gplots, ggplot2,
dplyr, magrittr, gdata

c(
person("Edmond", "Sanou", , "doedmond.sanou@univ-evry.fr", role = c("aut", "cre")),
person("Tung", "Le", role = "ctb"),
person("Christophe", "Ambroise", role = "ths"),
person("Geneviève", "Robin", role = "ths")
)
Inference of Multiscale graphical models with neighborhood
selection approach.  The method is based on solving a convex
optimization problem combining a Lasso and fused-group Lasso
penalties.  This allows to infer simultaneously a conditional
independence graph and a clustering partition. The optimization is
based on the Continuation with Nesterov smoothing in a
Shrinkage-Thresholding Algorithm solver (Hadj-Selem et al. 2018)
<doi:10.1109/TMI.2018.2829802> implemented in python.
corpcor, gridExtra, Matrix, methods, R.utils, reticulate,
stats
knitr, mvtnorm, rmarkdown, testthat (>= 3.0.0)
list( packages = list( list(package = c("")) ) )
Edmond Sanou [aut, cre],
Tung Le [ctb],
Christophe Ambroise [ths],
Geneviève Robin [ths]

corpcor, ggplot2, ggrepel, gridExtra, Matrix, methods,
R.utils, reticulate (>= 1.25), rstudioapi
list( packages = list( list(package = "scipy",
version = "1.7.1"), list(package = "numpy", version =
"1.22.4"), list(package = "matplotlib"), list(package =
"scikit-learn"), list(package = "six"), list(package =
"pylearn-parsimony", version = "0.3.1", pip = TRUE) ) )

Model Averaging for Multivariate GLM with Null Models
Masatoshi Katabuchi and Akihiro Nakamura
Tools for univariate and multivariate generalized linear models with model averaging and null model technique.

Functions to estimate and sample from a mixed Graphical model.

Estimation of Mixed Graphical model using L1-regularized neighborhood regression.