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echoice2 (version 0.2.5)

Choice Models with Economic Foundation

Description

Implements choice models based on economic theory, including estimation using Markov chain Monte Carlo (MCMC), prediction, and more. Its usability is inspired by ideas from 'tidyverse'. Models include versions of the Hierarchical Multinomial Logit and Multiple Discrete-Continous (Volumetric) models with and without screening. The foundations of these models are described in Allenby, Hardt and Rossi (2019) . Models with conjunctive screening are described in Kim, Hardt, Kim and Allenby (2022) . Models with set-size variation are described in Hardt and Kurz (2020) .

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Install

install.packages('echoice2')

Monthly Downloads

245

Version

0.2.5

License

MIT + file LICENSE

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Repository

https://github.com/ninohardt/echoice2

Homepage

http://ninohardt.de/echoice2/

Maintainer

Nino Hardt

Last Published

November 2nd, 2025

Functions in echoice2 (0.2.5)

ec_dem_aggregate

Aggregate posterior draws of demand

ec_trace_screen

Generate Screening probability traceplots

Obtain Log Marginal Density from draw objects

ec_estimates_SIGMA_corr

Obtain posterior mean estimates of upper level correlations

Convert "list of lists" format to long "tidy" format

Converts a set of dummy variables into a single categorical variable

ec_summarize_attrlvls

Summarize attributes and levels

Generate MU_theta traceplot

Create demand curves

Obtain attributes and levels from tidy choice data with dummies

ec_dem_summarise

Summarize posterior draws of demand

Get the attribute of an object

prep_newprediction

Match factor levels between two datasets

Log-Likelihood for compensatory volumetric demand model

ec_util_dummy_mutualeclusive

Find mutually exclusive columns

ec_estimates_screen

Summarize attribute-based screening parameters

Prepare choice data for analysis (without x being present)

icecream_discrete

icecream_discrete

echoice2-package

echoice2: Choice Models with Economic Foundation

ec_screen_summarise

Summarize posterior draws of screening

ec_screenprob_sr

Screening probabilities of choice alternatives

ec_undummy_yesno

Convert dummy-coded variables to yes/no factor

Demand Prediction (Volumetric Demand Model)

Thin 'echoice2'-vd draw objects

ec_demcurve_cond_dem

Create demand-incidence curves

Log Marginal Density (Newton-Raftery)

ec_demcurve_inci

Create demand-incidence curves

ec_gen_err_normal

Simulate error realization from Normal distribution

Simulate error realization from EV1 distribution

Demand Prediction (Volumetric demand, accounting for set-size variation, EV1 errors)

vd_dem_vdm_screen

Demand Prediction (Volumetric demand, attribute-based screening)

ec_undummy_lowhigh

Convert dummy-coded variables to low/high factor

ec_undummy_lowmediumhigh

Convert dummy-coded variables to low/medium/high factor

vd_LL_vdm_screen

Log-Likelihood for conjunctive-screening volumetric demand model

Estimate volumetric demand model

Log-Likelihood for volumetric demand model with set-size variation

Generate tidy choice data with dummies from long-format choice data

Add product id to demand draws

Prepare choice data for analysis

vd_dem_summarise

Summarize posterior draws of demand (volumetric models only)

ec_util_choice_to_long

Convert a vector of choices to long format

Estimate volumetric demand model accounting for set size variation (1st order)

vd_est_vdm_screen

Estimate volumetric demand model with attribute-based conjunctive screening

dd_est_hmnl_screen

Estimate discrete choice model (HMNL, attribute-based screening (not including price))

ec_boxplot_screen

Generate Screening probability boxplot

Dummy-code a categorical variable

Create dummy variables within a tibble

Estimate discrete choice model (HMNL)

Log-Likelihood for screening hmnl model

Discrete Choice Predictions (HMNL with attribute-based screening)

Evaluate (hold-out) demand predictions

Log-Likelihood for compensatory hmnl model

Generate MU_theta boxplot

Discrete Choice Predictions (HMNL)

ec_draws_screen

Obtain Screening probability draws

Obtain MU_theta draws

ec_estimates_SIGMA

Obtain posterior mean estimates of upper level covariance

ec_estimates_MU

Obtain upper level model estimates