Array CGH Data Analysis and Visualization
Provides functions to import data from Agilent CGH arrays and process them according to the cghRA workflow. Implements several algorithms such as WACA, STEPS and cnvScore and an interactive graphical interface.
tcltk, tkrplot, parallel, GLAD, graphics, grDevices

Segmentation methods for array CGH analysis
Franck Picard, Mark Hoebeke, Emilie Lebarbier, Guillem Rigaill,
Stephane Robin
cghseg is an R package dedicated to the analysis of CGH
profiles using segmentation models.

Franck Picard, Mark Hoebeke, Emilie Lebarbier, Vincent Miele,
Guillem Rigaill, Stephane Robin

Segmentation Methods for Array CGH Analysis
Orphaned on 2013-11-12 as the maintainer repeatedly
failed to respond to requests for valid portable C++ code.

Dedicated to the analysis of CGH (Comparative Genomic Hybridization) array profiles using segmentation models. 'cghseg' package is intended to detect breakpoints from CGH profiles. It can handle both single and multiple profiles analysis, to perform segmentation, normalization and calling. Methods for joint segmentation are described in : Picard and al. (2011), <doi:10.1093/biostatistics/kxq076>.

L1-Penalized Censored Gaussian Graphical Models
The l1-penalized censored Gaussian graphical model (cglasso) is an extension of the graphical lasso estimator developed to handle datasets with censored observations. An EM-like algorithm is implemented to estimate the parameters of the censored Gaussian graphical models.

The l1-penalized censored Gaussian graphical model is an extension of the graphical lasso estimator developed to handle datasets with censored observations. An EM-like algorithm is implemented to estimate the parameters of the censored Gaussian graphical models.

Conditional Graphical LASSO for Gaussian Graphical Models with
Censored and Missing Values
c(person(given = "Luigi", family = "Augugliaro", email = "luigi.augugliaro@unipa.it", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-4603-7541")),
person(given = "Gianluca", family = "Sottile", role = "aut", comment = c(ORCID = "0000-0001-9347-7251")),
person(given = "Ernst C.", family = "Wit", role = "aut", comment = c(ORCID = "0000-0002-3671-9610")),
person(given = "Veronica", family = "Vinciotti", role = "aut", comment = c(ORCID = "0000-0002-2625-7977")))
Conditional graphical lasso (cglasso) estimator is an extension of the graphical lasso proposed to estimate the conditional dependence structure of a set of p response variables given q predictors. This package provides suitable extensions developed to study datasets with censored and/or missing values. Standard conditional graphical lasso is available as a special case. Furthermore, cglasso package provides an integrated set of core routines for visualization, analysis, and simulation of datasets with censored and/or missing values drawn from a Gaussian graphical model.