Builds a positive definite matrix from an undirected graph G that can be used as a covariance matrix for a Gaussian graphical model with graph G. The inverse of the resulting matrix contains zeroes at the missing edges of the given undirected graph G.

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`g` |
undirected graph specified either as a |

`rho` |
real number between -1/(n.var-1) and 1 corresponding to the mean marginal correlation |

`matrix.completion` |
algorithm to employ in the matrix completion operations
employed to construct a positive definite matrix with the
zero pattern specified in |

`tol` |
tolerance under which the matrix completion algorithm stops. |

`verbose` |
show progress on the calculations. |

`R.code.only` |
logical; if FALSE then the faster C implementation is used in the internal call to the HTF, or IPF, algorithm (default); if TRUE then only R code is executed. |

The random covariance matrix is built by first generating a random matrix
with the function `qpRndWishart`

from a Wishart distribution
whose expected value is a matrix with unit diagonal and constant off-diagonal
entries equal to `rho`

.

A random positive definite matrix that can be used as a covariance matrix
for a Gaussian graphical model with graph `G`

.

A. Roverato

Tur, I., Roverato, A. and Castelo, R. Mapping eQTL networks with mixed graphical Markov models.
*Genetics*, 198(4):1377-1393, 2014.

`qpRndGraph`

`qpGetCliques`

`qpIPF`

`qpRndWishart`

`rmvnorm`

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