Man pages for davharris/mistnet2
Neural Networks with Latent Random Variables

activatorActivator objects and nonlinear activation functions
adjustableFlag a distribution parameter for optimization
backprop.mistnet_networkBackprop: calculate network gradients using backpropagation
draw_samplesDraw random samples from an object
draw_samples.distributionSample random numbers from a probability distribution
ENONormal distribution with empirical mean and variance
feedforward.networkFeed forward: calculate network state from its coefficients
get_valuesGet parameter values from a distribution object
gradCalculate the gradient of a distribution
inflate"inflate" a vector by repeating rows or columns
IUImproper uniform distribution
layerDescribe a layer of a neural network
log_probCalculate the log probability density of an object
log_prob.distributionCalculate the log probability of a distribution
log_prob.mistnet_networkCalculate the log-likelihood of a network object
make_distributionMake an 'distribution' from a gamlss distribution
mistnetBuild and fit a neural network with random effects
mistnet2Neural Networks with Latent Random Variables.
mistnet_fitFit a mistnet model
mistnet_fit_optimxOptimize a mistnet model using the 'optimx' package
predict.networkMake predictions from a trained network
davharris/mistnet2 documentation built on May 12, 2017, 7:42 p.m.