The function to generate random vectors from the Dirichlet distribution.

1 | ```
rdirichlet(n, shape)
``` |

`n` |
Number of Dirichlet random vectors to generate. If |

`shape` |
Vector with |

The Dirichlet distribution is the multidimensional generalization of the beta distribution.

A *k*-variate Dirichlet random vector *(x[1],…,x[k])* has
the joint probability density function

*
Γ(α[1]+…+α[k+1])(Γ(α[1])…Γ(α[k+1]))
x[1]^(α[1]-1)… x_k^(α[k]-1)(1-∑_{i=1}^k x[i])^(α[k+1]-1),*

where *x[i] ≥ 0* for all *i = 1, …, k*,
*∑_{i=1}^k x[i] ≤ 1*, and
*α[1], …, α[k+1]* are positive shape
parameters.

`rdirichlet`

generates the Dirichlet random vector by utilizing the transformation
method based on beta variates and three guidelines introduced by Hung *et al.* (2011).
The three guidelines include: how to choose the fastest beta generation algorithm, how to
best re-order the shape parameters, and how to reduce the amount of arithmetic operations.

`rdirichlet()`

returns a matrix with `n`

rows, each containing a single Dirichlet
random vector.

Ching-Wei Cheng <aks43725@gmail.com>,

Ying-Chao Hung <hungy@nccu.edu.tw>,

Narayanaswamy Balakrishnan <bala@univmail.cis.mcmaster.ca>

`rdirichlet`

uses a C translation of

Y. C. Hung and N. Balakrishnan and C. W. Cheng (2011),
Evaluation of algorithms for generating Dirichlet random vectors,
*Journal of Statistical Computation and Simulation*, **81**, 445–459.

Y. C. Hung and N. Balakrishnan and C. W. Cheng (2011),
Evaluation of algorithms for generating Dirichlet random vectors,
*Journal of Statistical Computation and Simulation*, **81**, 445–459.

`rdirichlet`

in package MCMCpack.

`rdirichlet`

in package gtools.

1 2 | ```
library(rBeta2009)
rdirichlet(10, c(1.5, 0.7, 5.2, 3.4))
``` |

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