Description Usage Arguments Details Value Note Author(s) References See Also Examples

The function `SN2()`

defines the Skew Normal Type 2 distribution, a three parameter distribution, for a `gamlss.family`

object to be used in GAMLSS fitting using the function `gamlss()`

, with parameters `mu`

, `sigma`

and `nu`

. The functions `dSN2`

, `pSN2`

, `qSN2`

and `rSN2`

define the density, distribution function, quantile function and random generation for the `SN2`

parameterization of the Skew Normal Type 2 distribution.

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`mu.link` |
Defines the |

`sigma.link` |
Defines the |

`nu.link` |
Defines the |

`x, q` |
vector of quantiles |

`mu` |
vector of location parameter values |

`sigma` |
vector of scale parameter values |

`nu` |
vector of scale parameter values |

`log, log.p` |
logical; if TRUE, probabilities p are given as log(p) |

`lower.tail` |
logical; if TRUE (default), probabilities are P[X <= x], otherwise P[X > x] |

`p` |
vector of probabilities |

`n` |
number of observations. If |

The parameterization of the Skew Normal Type 2 distribution in the function `SN2`

is ...

returns a gamlss.family object which can be used to fit a Skew Normal Type 2 distribution in the `gamlss()`

function.

This is a special case of the Skew Exponential Power type 3 distribution (`SEP3`

)where `tau=2`

.

Mikis Stasinopoulos, Bob Rigby and Fiona McElduff.

Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion),
*Appl. Statist.*, **54**, part 3, pp 507-554.

Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/).

Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R.
*Journal of Statistical Software*, Vol. **23**, Issue 7, Dec 2007, http://www.jstatsoft.org/v23/i07.

Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017)
*Flexible Regression and Smoothing: Using GAMLSS in R*, Chapman and Hall/CRC.

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