Description Usage Arguments Details Value

Fits a beta mixture model for any number of classes

1 |

`Y` |
Data matrix (n x j) on which to perform clustering |

`w` |
Initial weight matrix (n x k) representing classification |

`maxiter` |
Maximum number of EM iterations |

`tol` |
Convergence tolerance |

`weights` |
Case weights |

`verbose` |
Verbose output? |

Typically not be called by user.

A list of parameters representing mixture model fit, including posterior weights and log-likelihood

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