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