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 |
combineR |
Combine two networks. |
Cont2Gaus |
A transfomation from count data into Gaussian data |
ContSim |
A simulation method for generating count data from multivariate Zero-Inflated Negative Binomial distributions |
ContTran |
A data continuized transformation |
count |
An example of count dataset for constructing networks |
DAGsim |
Simulate a directed acyclic graph with mixed data (continuous and binary) |
diffR |
Detect difference between two networks. |
equSAR |
An equvalent mearsure of partial correlation coeffients |
JGGM |
Joint estimation of Multiple Gaussian Graphical Models |
mixed3000 |
One example dataset for p_learning |
pcorselR |
Multiple hypothesis test |
plotGraph |
Plot Single Network |
plotJGraph |
Plot Networks |
psical |
An calculation of psi scores. |
p_learning |
Construct Bayesian Network based on p-learning algorithm. |
simtoequiv |
Transform a directed acyclic graph into an equivalent correct graph. |
solcov |
Calculate covariance matrix and precision matrix |
SR0 |
One example dataset for equSA |
SR0_mat |
The adjacency matrix for SR0 dataset. |
TR0 |
One example dataset for equSA |
TR0_mat |
The adjacency matrix for TR0 dataset. |