Markov Chain Monte Carlo Maximum Likelihood for Generalised Linear Mixed Models

Documentation for package ‘glmmrMCML’ version 0.2.2

glmmrMCML-package	Markov Chain Monte Carlo Maximum Likelihood for Generalised Linear Mixed Models
aic_mcml	Calculates the conditional Akaike Information Criterion for the GLMM
gen_u_samples	Generate samples of random effects using MCMC
glmmrMCML	Markov Chain Monte Carlo Maximum Likelihood for Generalised Linear Mixed Models
mcmc_sample	Hamiltonian Monte Carlo Sampler for Model Random Effects
mcml_full	Markov Chain Monte Carlo Maximum Likelihood Algorithm
mcml_hess	Generate Hessian matrix of GLMM
mcml_hess_sparse	Generate Hessian matrix of GLMM using sparse matrix methods
mcml_la	Maximum Likelihood with Laplace Approximation and Derivative Free Optimisation
mcml_la_nr	Maximum Likelihood with Laplace Approximation and Newton-Raphson
mcml_optim	Likelihood maximisation for the GLMM
mcml_optim_sparse	Likelihood maximisation for the GLMM using sparse matrix methods
mcml_simlik	Simulated likelihood optimisation step for MCML
mcml_simlik_sparse	Simulated likelihood optimisation step for MCML using sparse matrix methods
mcnr_family	Returns the file name and type for MCNR function
ModelMCML	Extension to the Model class to use Markov Chain Monte Carlo Maximum Likelihood
mvn_ll	Multivariate normal log likelihood
print.mcml	Prints an mcml fit output
summary.mcml	Summarises an mcml fit output