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equSA (version 1.1.5)

Estimate Directed and Undirected Graphical Models and Construct Networks

Description

Provides an equivalent measure of partial correlation coefficients for high-dimensional Gaussian Graphical Models to learn and visualize the underlying relationships between variables from single or multiple datasets. You can refer to Liang, F., Song, Q. and Qiu, P. (2015) for more detail. Based on this method, the package also provides the method for constructing networks for Next Generation Sequencing Data, for jointly estimating multiple Gaussian Graphical Models and constructing directed acyclic graph (Bayesian Network).

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Version

Install

install.packages('equSA')

Monthly Downloads

30

Version

1.1.5

License

GPL-2

Maintainer

Bochao Jia

Last Published

January 20th, 2018

Functions in equSA (1.1.5)

JGGM

Joint estimation of Multiple Gaussian Graphical Models
SR0

One example dataset for equSA
Cont2Gaus

A transfomation from count data into Gaussian data
ContSim

A simulation method for generating count data from multivariate Zero-Inflated Negative Binomial distributions
TR0_mat

The adjacency matrix for TR0 dataset.
combineR

Combine two networks.
SR0_mat

The adjacency matrix for SR0 dataset.
TR0

One example dataset for equSA
ContTran

A data continuized transformation
DAGsim

Simulate a directed acyclic graph with mixed data (continuous and binary)
equSA-package

Graphical model has been widely used in may scientific fileds to describe the conditional independent relationships for a large set of random variables. Through this package, we provide tools to learn both undirected graph (Markov Random Field) and directed acyclic graph (Bayesian Network). p
equSAR

An equvalent mearsure of partial correlation coeffients
mixed3000

One example dataset for p_learning
p_learning

Construct Bayesian Network based on p-learning algorithm.
pcorselR

Multiple hypothesis test
plotGraph

Plot Single Network
simtoequiv

Transform a directed acyclic graph into an equivalent correct graph.
solcov

Calculate covariance matrix and precision matrix
plotJGraph

Plot Networks
psical

An calculation of \(\psi\) scores.
count

An example of count dataset for constructing networks
diffR

Detect difference between two networks.