# Fast computation of the multivariate Student's t density.

### Description

Fast computation of the multivariate Student's t density.

### Usage

1 |

### Arguments

`X` |
matrix n by d where each row is a d dimensional random vector. Alternatively |

`mu` |
vector of length d, representing the mean of the distribution. |

`sigma` |
scale matrix (d x d). Alternatively it can be the cholesky decomposition
of the scale matrix. In that case isChol should be set to TRUE. Notice that ff the degrees of
freedom (the argument |

`df` |
a positive scalar representing the degrees of freedom. |

`log` |
boolean set to true the logarithm of the pdf is required. |

`ncores` |
Number of cores used. The parallelization will take place only if OpenMP is supported. |

`isChol` |
boolean set to true is |

### Details

There are in fact many candidates for the multivariate generalization of Student's t-distribution, here we use
the parametrization described here https://en.wikipedia.org/wiki/Multivariate_t-distribution. NB: at the moment
the parallelization does not work properly on Solaris OS when `ncores>1`

. Hence, `dmvt()`

checks if the OS
is Solaris and, if this the case, it imposes `ncores==1`

.

### Value

A vector of length n where the i-the entry contains the pdf of the i-th random vector.

### Author(s)

Matteo Fasiolo <matteo.fasiolo@gmail.com>

### Examples

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