Function reference • echoice2

All functions
`dd_dem()`	Discrete Choice Predictions (HMNL)
`dd_dem_sr()`	Discrete Choice Predictions (HMNL with attribute-based screening)
`dd_est_hmnl()`	Estimate discrete choice model (HMNL)
`dd_est_hmnl_screen()`	Estimate discrete choice model (HMNL, attribute-based screening (not including price))
`dd_LL()`	Log-Likelihood for compensatory hmnl model
`dd_LL_sr()`	Log-Likelihood for screening hmnl model
`dummify()`	Create dummy variables within a tibble
`dummyvar()`	Dummy-code a categorical variable
`echoice2`	echoice2
`ec_boxplot_MU()`	Generate MU_theta boxplot
`ec_boxplot_screen()`	Generate Screening probability boxplot
`ec_demcurve()`	Create demand curves
`ec_demcurve_cond_dem()`	Create demand-incidence curves
`ec_demcurve_inci()`	Create demand-incidence curves
`ec_dem_aggregate()`	Aggregate posterior draws of demand
`ec_dem_eval()`	Evaluate (hold-out) demand predictions
`ec_dem_summarise()` `ec_dem_summarize()`	Summarize posterior draws of demand
`ec_draws_MU()`	Obtain MU_theta draws
`ec_draws_screen()`	Obtain Screening probability draws
`ec_estimates_MU()`	Obtain upper level model estimates
`ec_estimates_screen()`	Summarize attribute-based screening parameters
`ec_estimates_SIGMA()`	Obtain posterior mean estimates of upper level covariance
`ec_estimates_SIGMA_corr()`	Obtain posterior mean estimates of upper level correlations
`ec_gen_err_ev1()`	Simulate error realization from EV1 distribution
`ec_gen_err_normal()`	Simulate error realization from Normal distribution
`ec_lmd_NR()`	Obtain Log Marginal Density from draw objects
`ec_lol_tidy1()`	Convert "list of lists" format to long "tidy" format
`ec_screenprob_sr()`	Screening probabilities of choice alternatives
`ec_screen_summarise()` `ec_screen_summarize()`	Summarize posterior draws of screening
`ec_summarize_attrlvls()` `ec_summarise_attrlvls()`	Summarize attributes and levels
`ec_trace_MU()`	Generate MU_theta traceplot
`ec_trace_screen()`	Generate Screening probability traceplots
`ec_undummy()`	Converts a set of dummy variables into a single categorical variable
`ec_undummy_lowhigh()`	Convert dummy-coded variables to low/high factor
`ec_undummy_lowmediumhigh()`	Convert dummy-coded variables to low/medium/high factor
`ec_undummy_yesno()`	Convert dummy-coded variables to yes/no factor
`ec_util_choice_to_long()`	Convert a vector of choices to long format
`ec_util_dummy_mutualeclusive()`	Find mutually exclusive columns
`get_attr_lvl()`	Obtain attributes and levels from tidy choice data with dummies
`%.%`	Get the attribute of an object
`icecream`	icecream
`icecream_discrete`	icecream_discrete
`logMargDenNRu()`	Log Marginal Density (Newton-Raftery)
`pizza`	pizza
`prep_newprediction()`	Match factor levels between two datasets
`vd_add_prodid()`	Add product id to demand draws
`vd_dem_summarise()` `vd_dem_summarize()`	Summarize posterior draws of demand (volumetric models only)
`vd_dem_vdm()`	Demand Prediction (Volumetric Demand Model)
`vd_dem_vdm_screen()`	Demand Prediction (Volumetric demand, attribute-based screening)
`vd_dem_vdm_ss()`	Demand Prediction (Volumetric demand, accounting for set-size variation, EV1 errors)
`vd_est_vdm()`	Estimate volumetric demand model
`vd_est_vdm_screen()`	Estimate volumetric demand model with attribute-based conjunctive screening
`vd_est_vdm_ss()`	Estimate volumetric demand model accounting for set size variation (1st order)
`vd_LL_vdm()`	Log-Likelihood for compensatory volumetric demand model
`vd_LL_vdmss()`	Log-Likelihood for volumetric demand model with set-size variation
`vd_LL_vdm_screen()`	Log-Likelihood for conjunctive-screening volumetric demand model
`vd_long_tidy()`	Generate tidy choice data with dummies from long-format choice data
`vd_prepare()`	Prepare choice data for analysis
`vd_prepare_nox()`	Prepare choice data for analysis (without x being present)
`vd_thin_draw()`	Thin 'echoice2'-vd draw objects

Reference

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