Fast estimation of the value of *α*.

1 | ```
alfa.tune(x, B = 1, ncores = 1)
``` |

`x` |
A matrix with the compositional data. No zero vaues are allowed. |

`B` |
If no (bootstrap based) confidence intervals should be returned this should be 1 and more than 1 otherwise. |

`ncores` |
If ncores is greater than 1 parallel computing is performed. It is advisable to use it if you have many observations and or many variables, otherwise it will slow down th process. |

This is a faster function than `alfa.profile`

for choosing the value of *α*.

A vector with the best alpha, the maximised log-likelihood and the log-likelihood at *α=0*, when B = 1 (no bootstrap). If B>1 a list including:

`param` |
The best alpha and the value of the log-likelihod, along with the 95% bootstrap based confidence intervals. |

`message` |
A message with some information about the histogram. |

`runtime` |
The time (in seconds) of the process. |

Michail Tsagris

R implementation and documentation: Michail Tsagris <mtsagris@yahoo.gr> and Giorgos Athineou <athineou@csd.uoc.gr>

Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. http://arxiv.org/pdf/1106.1451.pdf

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