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# QData / SIMULE

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# SIMULE

This is an R implementation of the SIMULE algorithm proposed in the following paper:

"A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models", accepted by Machine Learning @ URL

Please run demo(simuleDemo) to learn the basic functions provided by this package. For further details, please read the original paper @ URL or read the R-package Manual: @ URL

• CRAN R Library page: URL

• Presentation @ URL

## Dependency

It depends on the following existing packages. To use them, simply

```library('pcaPP')
library('lpSolve')
library('parallel')```

If you don't have these packages, simply use

`install.packages('packageNameFromAbove')`

## Usage

1. install the R "simule" package through R console:
`install.packages('simule')`
1. then load the library simule in R console, by running:
`library(simule)`
1. Then, simply run the function `simule` on your favorite datasets For example,
`simule(CovarianceMatrixList, lambda = 0.05, epsilon = 0.5, parallel = TRUE)`

This function will returns a `list` (a data structure in R) of graphs estimated by the SIMULE package.

## Three possible types of inputs for the Argument `CovarianceMatrixList`

1. The argument `CovarianceMatrixList` can represent a `list` of data matrices directly: The i-th item ` CovarianceMatrixList[[i]]` represents the i-th matrix in a `list` of data matrices `CovarianceMatrixList`. ** Please make sure the order of the feature variables are the same among all the data matrices in `CovarianceMatrixList`.**

2. If the input `CovarianceMatrixList` is Symmetric, the package automatically assumes that the data inputs belong to the following two types:

• The argument `CovarianceMatrixList` can a `list` of covariance matrices. Assuming `X` represents a list of data matrices, whose i-th item ` X[[i]]` represents the data matrix of the i-th task.

We can use the following function to calculate the covariance matrices:

`CovarianceMatrixList[[i]] = cov(X[[i]])`
• The argument `CovarianceMatrixList` can represent a `list` of kendall's tau correlation matrices. The kendall's tau correlation matrices can be calculated by using the following command:
`cor.fk(X[[i]])  `

(by the `'pcaPP'` package.)

The kendall's tau correlation matrices can also be calculated through the following R functions:

`cor(X[[i]], method = 'kendall')`

However the above way of calculating kendall's tau correlation matrix is very slow in R.

## Other Arguments

• `lambda`

The parameter for the sparsity level of the estimated graphs. The larger `lambda` you choose, the sparser graphs you will estimate from the inputs.

• `epsilon`

The parameter reflects the differences of sparsity level between the shared subgraph versus the context-specific subgraphs. The larger `epsilon` you choose, the denser the shared subgraph is (while the context-specific subgraphs are sparser) and vice versa.

• `covType ` This parameter controls SIMULE estimates the sparse Gaussian Graphical models (sGGM) or the sparse Nonparanormal Graphical Models from the input data. This parameter matters only when the input argument `CovarianceMatrixList` represents a `list` of data matrices directly: When `covType = "cov"` the package estimates sGGMs from the input. When ` covType == "kendall"`, the package estimates sNGMs from the input.

• `parallel`

Logic parameter for parallel implementation or not. If you have a multi-core machine, let `parallel = TRUE`.

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