Estimate discrete choice model (HMNL)
Log-Likelihood for compensatory hmnl model
Generate Screening probability boxplot
Summarize posterior draws of demand
Aggregate posterior draws of demand
Discrete Choice Predictions (HMNL)
Estimate discrete choice model (HMNL, attribute-based screening (not including price))
Log-Likelihood for screening hmnl model
Discrete Choice Predictions (HMNL with attribute-based screening)
Evaluate (hold-out) demand predictions
Create demand curves
Summarize attributes and levels
Obtain upper level model estimates
Simulate error realization from EV1 distribution
Generate MU_theta traceplot
Obtain posterior mean estimates of upper level covariance
Obtain Log Marginal Density from draw objects
Obtain MU_theta draws
Create demand-incidence curves
Convert "list of lists" format to long "tidy" format
Generate Screening probability traceplots
Convert dummy-coded variables to yes/no factor
Simulate error realization from Normal distribution
Convert a vector of choices to long format
Convert dummy-coded variables to low/high factor
Summarize posterior draws of screening
Prepare choice data for analysis (without x being present)
Converts a set of dummy variables into a single categorical variable
Screening probabilities of choice alternatives
Create demand-incidence curves
Thin 'echoice2'-vd draw objects
ec_util_dummy_mutualeclusive
Find mutually exclusive columns
icecream
icecream_discrete
echoice2: Choice Models with Economic Foundation
Add product id to demand draws
Summarize attribute-based screening parameters
Obtain posterior mean estimates of upper level correlations
Estimate volumetric demand model with attribute-based conjunctive screening
Obtain attributes and levels from tidy choice data with dummies
Convert dummy-coded variables to low/medium/high factor
Match factor levels between two datasets
Log-Likelihood for compensatory volumetric demand model
Estimate volumetric demand model accounting for set size variation (1st order)
Summarize posterior draws of demand (volumetric models only)
Obtain Screening probability draws
Log Marginal Density (Newton-Raftery)
pizza
Demand Prediction (Volumetric Demand Model)
Demand Prediction (Volumetric demand, attribute-based screening)
Generate tidy choice data with dummies from long-format choice data
Get the attribute of an object
Prepare choice data for analysis
Log-Likelihood for conjunctive-screening volumetric demand model
Log-Likelihood for volumetric demand model with set-size variation
Demand Prediction (Volumetric demand, accounting for set-size variation, EV1 errors)
Estimate volumetric demand model
Create dummy variables within a tibble
Generate MU_theta boxplot
Dummy-code a categorical variable