apollo0.3.4 package

Tools for Choice Model Estimation and Application

apollo_addCovariance

Adds covariance matrix to Apollo model

apollo_attach

Attaches predefined variables.

apollo_avgInterDraws

Averages across inter-individual draws.

apollo_avgIntraDraws

Averages across intra-individual draws.

apollo_basTest

Ben-Akiva & Swait test

apollo_bootstrap

Bootstrap a model

apollo_checkArguments

Checks definitions of Apollo functions

apollo_choiceAnalysis

Reports market share for subsamples

apollo_classAlloc

Calculates class allocation probabilities for a Latent Class model

apollo_cnl

Calculates Cross-Nested Logit probabilities

apollo_combineModels

Combines separate model components.

apollo_combineResults

Write model results to file

apollo_compareInputs

Compares the content of apollo_inputs to their counterparts in the glo...

apollo_conditionals

Calculates conditionals

apollo_deltaMethod

Delta method for Apollo models

apollo_detach

Detaches parameters and the database.

apollo_dft

Calculate DFT probabilities

apollo_diagnostics

Pre-process input for common models return

apollo_dVdB

Calculates gradients of utility functions

apollo_dVdBOld

Calculates gradients of utility functions

apollo_el

Calculates Exploded Logit probabilities

apollo_emdc

MDC model with exogenous budget

apollo_emdc1

MDC model with exogenous budget

apollo_emdc2

Extended MDC

apollo_estimate

Estimates model

apollo_estimateHB

Estimates model using Bayesian estimation

apollo_expandLoop

Expands loops in a function or expression

apollo_firstRow

Keeps only the first row for each individual

apollo_fitsTest

Compares log-likelihood of model across categories

apollo_fmnl

Calculates Fractional Multinomial Logit probabilities

apollo_fnl

Calculates Fractional Nested Logit probabilities

apollo_initialise

Prepares environment

apollo_insertComponentName

Adds componentName2 to model calls

apollo_insertFunc

Modifies function to make it compatible with analytic gradients

apollo_insertOLList

Replaces tau=c(...) by tau=list(...) in calls to apollo_ol

apollo_insertRows

Inserts rows

apollo_insertRRMQuotes

Introduces quotes into rrm_settings

apollo_insertScaling

Scales variables inside a function

apollo_keepRows

Keeps only some rows

apollo_lc

Calculates the likelihood of a latent class model

apollo_lcConditionals

Calculates conditionals for latent class models.

apollo_lcEM

Uses EM for latent class model

apollo_lcUnconditionals

Returns unconditionals for a latent class model model

apollo_llCalc

Calculates log-likelihood of all model components

apollo_loadModel

Loads model from file

apollo_longToWide

Converts data from long to wide format.

apollo_lrTest

Likelihood ratio test

apollo_makeCluster

Creates cluster for estimation.

apollo_makeDraws

Creates draws for models with mixing

apollo_makeGrad

Creates gradient function.

apollo_makeHessian

Creates hessian function.

apollo_makeLogLike

Creates log-likelihood function.

apollo_mdcev

Calculates MDCEV likelihoods

apollo_mdcev2

Calculates MDCEV likelihoods

apollo_mdcnev

Calculates MDCNEV likelihoods

apollo_mixConditionals

Calculates conditionals for continuous mixture models

apollo_mixEM

Uses EM for models with continuous random coefficients

apollo_mixUnconditionals

Returns draws for continuously distributed random parameters in mixtur...

apollo_mlhs

Generate random draws using MLHS algorithm

apollo_mnl

Calculates Multinomial Logit probabilities

apollo_modelOutput

Prints estimation results to console

apollo_modifyUserDefFunc

Checks and modifies Apollo user-defined functions

apollo_nl

Calculates Nested Logit probabilities

apollo_normalDensity

Calculates density for a Normal distribution

apollo_ol

Calculates Ordered Logit probabilities

apollo_op

Calculates Ordered Probit probabilities

apollo_outOfSample

Cross-validation of fit (LL)

apollo_ownModel

Calculates own model probabilities

apollo_panelProd

Calculates product across observations from same individual.

apollo_prediction

Predicts using an estimated model

apollo_prepareProb

Checks likelihood function

apollo_preprocess

Pre-process input for multiple models return

apollo_print

Prints message to terminal

apollo_readBeta

Reads parameters from file

apollo_rrm

Calculates Random Regret Minimisation model probabilities

apollo_saveOutput

Saves estimation results to files.

apollo_searchStart

Searches for better starting values.

apollo_setRows

Sets specified rows to a given value

apollo_sharesTest

Compares predicted and observed shares

apollo_sink

Starts or stops writing output to a text file.

apollo_speedTest

Measures evaluation time of a model

apollo_tobit

Calculates density for a Tobit model (censored Normal)

apollo_unconditionals

Returns unconditionals for models with random heterogeneity

apollo_validate

Pre-process input for common models return

apollo_validateControl

Validates apollo_control

apollo_validateData

Validates data

apollo_validateHBControl

Validates the apollo_HB list of parameters

apollo_validateInputs

Prepares input for apollo_estimate

apollo_varcov

Calculates variance-covariance matrix of an Apollo model

apollo_varList

Lists variable names and definitions used inside a function

apollo_weighting

Applies weights

apollo_writeF12

Writes an F12 file

apollo_writeTheta

Writes the vector [beta,ll] to a file called `modelname_iterations.csv...

aux_validateRows

Validates and expands rows if necessary.

dot-onAttach

Prints package startup message

print.apollo

Prints brief summary of Apollo model

summary.apollo

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.

  • Maintainer: Stephane Hess
  • License: GPL-2
  • Last published: 2024-10-01