Graphical User Interface of the mi Package
Daniel Lee <bearlee@alum.mit.edu>, Yu-Sung Su
<ys463@columbia.edu>

Daniel Lee <bearlee@alum.mit.edu>,
Yu-Sung Su <suyusung@tsinghua.edu.cn>

Graphical User Interface to the 'mi' Package
c(person("Bob", "Carpenter", email = "gelman@stat.columbia.edu", role = "ctb"),
person("Ben", "Goodrich", email = "benjamin.goodrich@columbia.edu", role = c("cre", "aut")),
person("Yu-Sung", "Su", email = "sophie2012@gmail.com", role = "ctb"))
This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data.
Bob Carpenter [ctb],
Ben Goodrich [cre, aut],
Yu-Sung Su [ctb]

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>.
c(person("Nadir", "Sella", role = c("aut","cre"), email = "nadir.sella@curie.fr"),
person("Louis", "Verny",role = "aut"),
person("Severine", "Affeldt", role = "aut"),
person("Hervé", "Isambert", role = c("aut"), email = "Herve.Isambert@curie.fr"))
MASS,igraph, methods, plotrix, bnlearn, Rcpp, ppcor
Nadir Sella [aut, cre],
Louis Verny [aut],
Severine Affeldt [aut],
Hervé Isambert [aut]

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