Neural Networks with Latent Random Variables

activator | Activator objects and nonlinear activation functions |

adjustable | Flag a distribution parameter for optimization |

backprop.mistnet_network | Backprop: calculate network gradients using backpropagation |

draw_samples | Draw random samples from an object |

draw_samples.distribution | Sample random numbers from a probability distribution |

ENO | Normal distribution with empirical mean and variance |

feedforward.network | Feed forward: calculate network state from its coefficients |

get_values | Get parameter values from a distribution object |

grad | Calculate the gradient of a distribution |

inflate | "inflate" a vector by repeating rows or columns |

IU | Improper uniform distribution |

layer | Describe a layer of a neural network |

log_prob | Calculate the log probability density of an object |

log_prob.distribution | Calculate the log probability of a distribution |

log_prob.mistnet_network | Calculate the log-likelihood of a network object |

make_distribution | Make an 'distribution' from a gamlss distribution |

mistnet | Build and fit a neural network with random effects |

mistnet2 | Neural Networks with Latent Random Variables. |

mistnet_fit | Fit a mistnet model |

mistnet_fit_optimx | Optimize a mistnet model using the 'optimx' package |

predict.network | Make predictions from a trained network |

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