Man pages for davharris/mistnet2
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 |
