Tools for Choice Model Estimation and Application
Adds covariance matrix to Apollo model
Attaches predefined variables.
Averages across inter-individual draws.
Averages across intra-individual draws.
Ben-Akiva & Swait test
Bootstrap a model
Checks definitions of Apollo functions
Reports market share for subsamples
Calculates class allocation probabilities for a Latent Class model
Calculates Cross-Nested Logit probabilities
Combines separate model components.
Write model results to file
Compares the content of apollo_inputs to their counterparts in the glo...
Calculates conditionals
Delta method for Apollo models
Detaches parameters and the database.
Calculate DFT probabilities
Pre-process input for common models return
Calculates gradients of utility functions
Calculates gradients of utility functions
Calculates Exploded Logit probabilities
MDC model with exogenous budget
MDC model with exogenous budget
Extended MDC
Estimates model
Estimates model using Bayesian estimation
Expands loops in a function or expression
Keeps only the first row for each individual
Compares log-likelihood of model across categories
Calculates Fractional Multinomial Logit probabilities
Calculates Fractional Nested Logit probabilities
Prepares environment
Adds componentName2 to model calls
Modifies function to make it compatible with analytic gradients
Replaces tau=c(...)
by tau=list(...)
in calls to apollo_ol
Inserts rows
Introduces quotes into rrm_settings
Scales variables inside a function
Keeps only some rows
Calculates the likelihood of a latent class model
Calculates conditionals for latent class models.
Uses EM for latent class model
Returns unconditionals for a latent class model model
Calculates log-likelihood of all model components
Loads model from file
Converts data from long to wide format.
Likelihood ratio test
Creates cluster for estimation.
Creates draws for models with mixing
Creates gradient function.
Creates hessian function.
Creates log-likelihood function.
Calculates MDCEV likelihoods
Calculates MDCEV likelihoods
Calculates MDCNEV likelihoods
Calculates conditionals for continuous mixture models
Uses EM for models with continuous random coefficients
Returns draws for continuously distributed random parameters in mixtur...
Generate random draws using MLHS algorithm
Calculates Multinomial Logit probabilities
Prints estimation results to console
Checks and modifies Apollo user-defined functions
Calculates Nested Logit probabilities
Calculates density for a Normal distribution
Calculates Ordered Logit probabilities
Calculates Ordered Probit probabilities
Cross-validation of fit (LL)
Calculates own model probabilities
Calculates product across observations from same individual.
Predicts using an estimated model
Checks likelihood function
Pre-process input for multiple models return
Prints message to terminal
Reads parameters from file
Calculates Random Regret Minimisation model probabilities
Saves estimation results to files.
Searches for better starting values.
Sets specified rows to a given value
Compares predicted and observed shares
Starts or stops writing output to a text file.
Measures evaluation time of a model
Calculates density for a Tobit model (censored Normal)
Returns unconditionals for models with random heterogeneity
Pre-process input for common models return
Validates apollo_control
Validates data
Validates the apollo_HB
list of parameters
Prepares input for apollo_estimate
Calculates variance-covariance matrix of an Apollo model
Lists variable names and definitions used inside a function
Applies weights
Writes an F12 file
Writes the vector [beta,ll] to a file called `modelname_iterations.csv...
Validates and expands rows if necessary.
Prints package startup message
Prints brief summary of Apollo model
Prints summary of Apollo model
Choice models are a widely used technique across numerous scientific disciplines. The Apollo package is a very flexible tool for the estimation and application of choice models in R. Users are able to write their own model functions or use a mix of already available ones. Random heterogeneity, both continuous and discrete and at the level of individuals and choices, can be incorporated for all models. There is support for both standalone models and hybrid model structures. Both classical and Bayesian estimation is available, and multiple discrete continuous models are covered in addition to discrete choice. Multi-threading processing is supported for estimation and a large number of pre and post-estimation routines, including for computing posterior (individual-level) distributions are available. For examples, a manual, and a support forum, visit <http://www.ApolloChoiceModelling.com>. For more information on choice models see Train, K. (2009) <isbn:978-0-521-74738-7> and Hess, S. & Daly, A.J. (2014) <isbn:978-1-781-00314-5> for an overview of the field.