This package provides the necessary functions for
performing the Partial Correlation coefficient with Information
Theory (PCIT) algorithm developed by Reverter and Chan (2008).
The PCIT algorithm identifies meaningful correlations to define
edges in a weighted network. The algorithm can be applied to
any correlation-based network including but not limited to gene
co-expression networks.

Partial Correlation Coefficient with Information Theory
Apply Partial Correlation coefficient with Information
Theory (PCIT) to a correlation matrix.
The PCIT algorithm identifies meaningful correlations to define
edges in a weighted network. The algorithm can be applied to
any correlation-based network including but not limited to gene
co-expression networks.
To reduce compute time by making use of multiple compute cores,
simply run PCIT on a computer with has multiple cores and also
has the Rmpi package installed. PCIT will then auto-detect the
multicore environment and run in parallel mode without the need
to rewrite your scripts. This makes scripts, using PCIT, portable
across single core (or no Rmpi package installed) computers
which will run in serial mode and multicore (with Rmpi package
installed) computers which will run in parallel mode.

c(person(given = "Andrew",
family = "Ying",
role = c("aut", "cre"),
email = "aying9339@gmail.com"),
person(given = "Yifan",
family = "Cui",
role = "ctb"),
person(given = "AmirEmad",
family = "Ghassami",
role = "ctb"))
We fit causal models using proxies. We implement two stage proximal least squares estimator. E.J. Tchetgen Tchetgen, A. Ying, Y. Cui, X. Shi, and W. Miao. (2020). An Introduction to Proximal Causal Learning. arXiv e-prints, arXiv-2009 <arXiv:2009.10982>.
Andrew Ying [aut, cre],
Yifan Cui [ctb],
AmirEmad Ghassami [ctb]

Group Regression Models for Risk Protein Complex Identification
Two protein complex-based group regression models (PCLasso and PCLasso2) for risk protein complex identification. PCLasso is a prognostic model that identifies risk protein complexes associated with survival. PCLasso2 is a classification model that identifies risk protein complexes associated with classes. For more information, see Wang and Liu (2021) <doi:10.1093/bib/bbab212>.