Description Usage Arguments Details Value Author(s) Examples

Computes cumulative distribution function (cdf) of the mixture model. The general form for the cdf of the mixture model is given by

*F(x,{Θ}) = ∑_{j=1}^{K}ω_j F(x,θ_j),*

where *Θ=(θ_1,…,θ_K)^T*, is the whole parameter vector, *θ_j* for *j=1,…,K* is the parameter space of the *j*-th component, i.e. *θ_j=(α_j,β_j)^{T}*, *F_j(.,θ_j)* is the cdf of the *j*-th component, and known constant *K* is the number of components. The vector of mixing parameters is given by *ω=(ω_1,…,ω_K)^T* where *ω_j*s sum to one, i.e., *∑_{j=1}^{K}ω_j=1*. Parameters *α* and *β* are the shape and scale parameters or both are the shape parameters. In the latter case, the parameters *α* and *β* are called the first and second shape parameters, respectively. The families considered for each component include Birnbaum-Saunders, Burr type XII, Chen, F, Frechet, Gamma, Gompertz, Log-normal, Log-logistic, Lomax, skew-normal, and Weibull.

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`data` |
Vector of observations. |

`g` |
Name of the family including: " |

`K` |
Number of components. |

`param` |
Vector of the |

For the skew-normal case, *α*, *β*, and *λ* are the location, scale, and skewness parameters, respectively.

A vector of the same length as `data`

, giving the cdf of the mixture model computed at `data`

.

Mahdi Teimouri

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