Choice Models with Economic Foundation
Discrete Choice Predictions (HMNL with attribute-based screening)
Discrete Choice Predictions (HMNL)
Estimate discrete choice model (HMNL, attribute-based screening (not i...
Estimate discrete choice model (HMNL)
Log-Likelihood for screening hmnl model
Log-Likelihood for compensatory hmnl model
Create dummy variables within a tibble
Dummy-code a categorical variable
Generate MU_theta boxplot
Generate Screening probability boxplot
Aggregate posterior draws of demand
Evaluate (hold-out) demand predictions
Summarize posterior draws of demand
Create demand-incidence curves
Create demand-incidence curves
Create demand curves
Obtain MU_theta draws
Obtain Screening probability draws
Obtain upper level model estimates
Summarize attribute-based screening parameters
Obtain posterior mean estimates of upper level correlations
Obtain posterior mean estimates of upper level covariance
Simulate error realization from EV1 distribution
Simulate error realization from Normal distribution
Obtain Log Marginal Density from draw objects
Convert "list of lists" format to long "tidy" format
Summarize posterior draws of screening
Screening probabilities of choice alternatives
Summarize attributes and levels
Generate MU_theta traceplot
Generate Screening probability traceplots
Convert dummy-coded variables to low/high factor
Convert dummy-coded variables to low/medium/high factor
Convert dummy-coded variables to yes/no factor
Converts a set of dummy variables into a single categorical variable
Convert a vector of choices to long format
Find mutually exclusive columns
echoice2: Choice Models with Economic Foundation
Obtain attributes and levels from tidy choice data with dummies
Get the attribute of an object
Log Marginal Density (Newton-Raftery)
Match factor levels between two datasets
Add product id to demand draws
Summarize posterior draws of demand (volumetric models only)
Demand Prediction (Volumetric demand, attribute-based screening)
Demand Prediction (Volumetric demand, accounting for set-size variatio...
Demand Prediction (Volumetric Demand Model)
Estimate volumetric demand model with attribute-based conjunctive scre...
Estimate volumetric demand model accounting for set size variation (1s...
Estimate volumetric demand model
Log-Likelihood for conjunctive-screening volumetric demand model
Log-Likelihood for compensatory volumetric demand model
Log-Likelihood for volumetric demand model with set-size variation
Generate tidy choice data with dummies from long-format choice data
Prepare choice data for analysis (without x being present)
Prepare choice data for analysis
Thin 'echoice2'-vd draw objects
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) <doi:10.1016/bs.hem.2019.04.002>. Models with conjunctive screening are described in Kim, Hardt, Kim and Allenby (2022) <doi:10.1016/j.ijresmar.2022.04.001>. Models with set-size variation are described in Hardt and Kurz (2020) <doi:10.2139/ssrn.3418383>.
Useful links