Simulated Maximum Likelihood Estimation of Mixed Logit Models for Large Datasets

Documentation for package ‘mixl’ version 1.3.4

mixl-package	Estimate mixed multinomial logit models
av_matrix	Extract the availabilites matrix from the dataset, using column indicies
check_draw_inputs	Check the inputs to the draw function
check_inputs	Check the inputs to the estimate function
compileUtilityFunction	compileUtilityFunction Deprecated, please see 'specify_model()'
create_halton_draws	Create a standard set of Halton draws to use in estimation
estimate	Runs a maximum likelihood estimation on a mixl choice model
extract_av_cols	Extract the availabilites matrix from the dataset using a column name prefix
extract_indiv_data	Extract the individual level data from the dataset for use in posterior analysis
generate_default_availabilities	Generate a ones-matrix of availabilities
mixl	Estimate mixed multinomial logit models
posteriors	Calculate the posteriors for a specified and estimated model
print.mixl	Prints the output of a model
print.summary.mixl	Print a model summary
probabilities	Calculate the probabilities for a specified and estimated model. Note that if new data or draws are provided, the model will not be re-estimated
specify_model	Validate the utility functions against the dataset and generate the optimised logliklihood function
summary.mixl	Create a model summary
summary_tex	Return tex formatted output of a model summary. If an output_file parameter is provided, save the object to that location
utilities	Return the the utilities for a set of coefficients
vcov.mixl	Calculates the Variance-Covariance Matrix of the mixl summary