Title: Graphical User Interface of the mi Package
Author: Daniel Lee <bearlee@alum.mit.edu>, Yu-Sung Su
Depends: tcltk, tkrplot, mi (>= 0.8-08), foreign
Description: Graphical User Interface of the mi Package

Depends: tcltk, tkrplot, mi (>= 0.9-01), foreign

Depends: tcltk, tkrplot, mi (>= 0.9-02), foreign

Depends: tcltk, tkrplot, mi (>= 0.9-06), foreign

Title: Graphical User Interface to the 'mi' Package
Authors@R: c(person("Bob", "Carpenter", email = "gelman@stat.columbia.edu", role = "ctb"),
person("Yu-Sung", "Su", email = "sophie2012@gmail.com", role = "ctb"))
Description: This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data.

Title: Multivariate Information Inductive Causation
Description: We report an information-theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found in many datasets. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. This approach can be applied on a wide range of datasets and provide new biological insights on regulatory networks from single cell expression data, genomic alterations during tumor development and co-evolving residues in protein structures. For more information you can refer to: Verny et al. Plos Comput Biol. (2017) <doi:10.1371/journal.pcbi.1005662>.
Authors@R: c(person("Nadir", "Sella", role = c("aut","cre"), email = "nadir.sella@curie.fr"),
person("Severine", "Affeldt", role = "aut"),
person("Hervé", "Isambert", role = c("aut"), email = "Herve.Isambert@curie.fr"))
Imports: MASS,igraph, methods, plotrix, bnlearn, Rcpp, ppcor

Title: Learning Causal or Non-Causal Graphical Models Using Information
Imports: MASS, igraph, bnlearn, ppcor, stats, Rcpp