diffee {diffee}  R Documentation 
Estimate DIFFerential networks via an Elementary Estimator under a highdimensional situation. Please run demo(diffee) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018) <arXiv:1710.11223>.
diffee(C, D, lambda = 0.05, covType = "cov", thre = "soft")
C 
A input matrix for the 'control' group. It can be data matrix or covariance matrix. If C is a symmetric matrix, the matrices are assumed to be covariance matrix. More details at <https://github.com/QData/DIFFEE> 
D 
A input matrix for the 'disease' group. It can be data matrix or covariance matrix. If D is a symmetric matrix, the matrices are assumed to be covariance matrix. More details at <https://github.com/QData/DIFFEE> 
lambda 
A positive number. The hyperparameter controls the sparsity
level of the matrices. The 
covType 
A parameter to decide which Graphical model we choose to estimate from the input data. If covType = "cov", it means that we estimate multiple sparse Gaussian Graphical models. This option assumes that we calculate (when input X represents data directly) or use (when X elements are symmetric representing covariance matrices) the sample covariance matrices as input to the simule algorithm. If covType = "kendall", it means that we estimate multiple nonparanormal Graphical models. This option assumes that we calculate (when input X represents data directly) or use (when X elements are symmetric representing correlation matrices) the kendall's tau correlation matrices as input to the simule algorithm. 
thre 
A parameter to decide which threshold function to use for

The DIFFEE algorithm is a fast and scalable Learning algorithm of Sparse Changes in HighDimensional Gaussian Graphical Model Structure. It solves the following equation:
\min\limits_{\Delta}\Delta_1
Subject to :
([T_v(\hat{\Sigma}_{d})]^{1} 
[T_v(\hat{\Sigma}_{c})]^{1})_{\infty} \le \lambda_n
Please also see the
equation (2.11) in our paper. The \lambda_n
is the hyperparameter
controlling the sparsity level of the matrix and it is the lambda
in
our function. For further details, please see our paper: Beilun Wang,
Arshdeep Sekhon, Yanjun Qi (2018) <arXiv:1710.11223>.
diffNet 
A matrix of the estimated sparse changes between two Gaussian Graphical Models 
Beilun Wang
Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018). Fast and Scalable Learning of Sparse Changes in HighDimensional Gaussian Graphical Model Structure. <arXiv:1710.11223>
## Not run:
data(exampleData)
result = diffee(exampleData[[1]], exampleData[[2]], 0.45)
plot.diffee(result)
## End(Not run)