In chupan1218/Informeasure: R implementation of informtion measures

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
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Informeasure

The goal of Informeasure is to quantify nonlinear dependence between variables in biological regulatory network inferences. This package compiles most of the information measures currently available: mutual information (MI), conditional mutual information (CMI)[1], interaction information (II)[2], partial information decomposition (PID)[3] and part mutual information (PMI)[4], all of which end with .measure() in form. They are MI.measure() for MI, CMI.measure() for CMI, II.measure() for II, PID.measure() for PID and PMI.measure() for PMI. The first estimator is used to infer bivariate networks while the last four are dedicated to analysis of trivariate networks. I here consider estimating information measures from breast cancer expression profile data generated by The Cancer Genome Atlas (TCGA), with applications in various types of transcriptome regulatory network inferences.

Installation

You can install the development version of Informeasure like so:

# FILL THIS IN! HOW CAN PEOPLE INSTALL YOUR DEV PACKAGE?

Example

This is a basic example which shows you how to solve a common problem:

library(Informeasure)
## basic example code

What is special about using README.Rmd instead of just README.md? You can include R chunks like so:

summary(cars)

You'll still need to render README.Rmd regularly, to keep README.md up-to-date. devtools::build_readme() is handy for this.

You can also embed plots, for example:

plot(pressure)

In that case, don't forget to commit and push the resulting figure files, so they display on GitHub and CRAN.

chupan1218/Informeasure documentation built on Jan. 19, 2024, 5:30 p.m.