{
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  "Package": "echoice2",
  "Title": "Choice Models with Economic Foundation",
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  "Date": "2025-11-02",
  "Authors@R": "person(given = \"Nino\",\nfamily = \"Hardt\",\nrole = c(\"aut\", \"cre\"),\nemail = \"me@ninohardt.com\",\ncomment = c(ORCID = \"0000-0001-5215-6216\"))",
  "Maintainer": "Nino Hardt <me@ninohardt.com>",
  "Description": "Implements choice models based on economic theory,\nincluding estimation using Markov chain Monte Carlo (MCMC),\nprediction, and more. Its usability is inspired by ideas from\n'tidyverse'. Models include versions of the Hierarchical\nMultinomial Logit and Multiple Discrete-Continous (Volumetric)\nmodels with and without screening. The foundations of these\nmodels are described in Allenby, Hardt and Rossi (2019)\n<doi:10.1016/bs.hem.2019.04.002>. Models with conjunctive\nscreening are described in Kim, Hardt, Kim and Allenby (2022)\n<doi:10.1016/j.ijresmar.2022.04.001>. Models with set-size\nvariation are described in Hardt and Kurz (2020)\n<doi:10.2139/ssrn.3418383>.",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/ninohardt/echoice2,\nhttp://ninohardt.de/echoice2/",
  "BugReports": "https://github.com/ninohardt/echoice2/issues",
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  "Date/Publication": "2025-11-02 23:24:36 UTC",
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    "ec_demcurve_cond_dem",
    "ec_demcurve_inci",
    "ec_draws_MU",
    "ec_draws_screen",
    "ec_estimates_MU",
    "ec_estimates_screen",
    "ec_estimates_SIGMA",
    "ec_estimates_SIGMA_corr",
    "ec_gen_err_ev1",
    "ec_gen_err_normal",
    "ec_lmd_NR",
    "ec_lol_tidy1",
    "ec_screen_summarise",
    "ec_screen_summarize",
    "ec_screenprob_sr",
    "ec_summarise_attrlvls",
    "ec_summarize_attrlvls",
    "ec_trace_MU",
    "ec_trace_screen",
    "ec_undummy",
    "ec_undummy_lowhigh",
    "ec_undummy_lowmediumhigh",
    "ec_undummy_yesno",
    "ec_util_choice_to_long",
    "ec_util_dummy_mutualeclusive",
    "get_attr_lvl",
    "logMargDenNRu",
    "prep_newprediction",
    "vd_add_prodid",
    "vd_dem_summarise",
    "vd_dem_summarize",
    "vd_dem_vdm",
    "vd_dem_vdm_screen",
    "vd_dem_vdm_ss",
    "vd_est_vdm",
    "vd_est_vdm_screen",
    "vd_est_vdm_ss",
    "vd_LL_vdm",
    "vd_LL_vdm_screen",
    "vd_LL_vdmss",
    "vd_long_tidy",
    "vd_prepare",
    "vd_prepare_nox",
    "vd_thin_draw"
  ],
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      "title": "icecream",
      "object": "icecream",
      "file": "icecream.rdata",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
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        "task",
        "alt",
        "x",
        "p",
        "Brand",
        "Flavor",
        "Size"
      ],
      "rows": 39600,
      "table": true,
      "tojson": true
    },
    {
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      "title": "icecream_discrete",
      "object": "icecream_discrete",
      "file": "icecream_discrete.rdata",
      "class": [
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        "tbl",
        "data.frame"
      ],
      "fields": [
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        "task",
        "alt",
        "x",
        "p",
        "Brand",
        "Flavor",
        "Size"
      ],
      "rows": 43200,
      "table": true,
      "tojson": true
    },
    {
      "name": "pizza",
      "title": "pizza",
      "object": "pizza",
      "file": "pizza.rdata",
      "class": [
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        "tbl",
        "data.frame"
      ],
      "fields": [
        "id",
        "task",
        "alt",
        "x",
        "p",
        "brand",
        "size",
        "crust",
        "topping",
        "coverage",
        "cheese"
      ],
      "rows": 12240,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "grapes-.-grapes",
      "title": "Get the attribute of an object",
      "topics": [
        "%.%"
      ]
    },
    {
      "page": "dd_dem",
      "title": "Discrete Choice Predictions (HMNL)",
      "topics": [
        "dd_dem"
      ]
    },
    {
      "page": "dd_dem_sr",
      "title": "Discrete Choice Predictions (HMNL with attribute-based screening)",
      "topics": [
        "dd_dem_sr"
      ]
    },
    {
      "page": "dd_est_hmnl",
      "title": "Estimate discrete choice model (HMNL)",
      "topics": [
        "dd_est_hmnl"
      ]
    },
    {
      "page": "dd_est_hmnl_screen",
      "title": "Estimate discrete choice model (HMNL, attribute-based screening (not including price))",
      "topics": [
        "dd_est_hmnl_screen"
      ]
    },
    {
      "page": "dd_LL",
      "title": "Log-Likelihood for compensatory hmnl model",
      "topics": [
        "dd_LL"
      ]
    },
    {
      "page": "dd_LL_sr",
      "title": "Log-Likelihood for screening hmnl model",
      "topics": [
        "dd_LL_sr"
      ]
    },
    {
      "page": "dummify",
      "title": "Create dummy variables within a tibble",
      "topics": [
        "dummify"
      ]
    },
    {
      "page": "dummyvar",
      "title": "Dummy-code a categorical variable",
      "topics": [
        "dummyvar"
      ]
    },
    {
      "page": "ec_boxplot_MU",
      "title": "Generate MU_theta boxplot",
      "topics": [
        "ec_boxplot_MU"
      ]
    },
    {
      "page": "ec_boxplot_screen",
      "title": "Generate Screening probability boxplot",
      "topics": [
        "ec_boxplot_screen"
      ]
    },
    {
      "page": "ec_dem_aggregate",
      "title": "Aggregate posterior draws of demand",
      "topics": [
        "ec_dem_aggregate"
      ]
    },
    {
      "page": "ec_dem_eval",
      "title": "Evaluate (hold-out) demand predictions",
      "topics": [
        "ec_dem_eval"
      ]
    },
    {
      "page": "ec_dem_summarise",
      "title": "Summarize posterior draws of demand",
      "topics": [
        "ec_dem_summarise",
        "ec_dem_summarize"
      ]
    },
    {
      "page": "ec_demcurve",
      "title": "Create demand curves",
      "topics": [
        "ec_demcurve"
      ]
    },
    {
      "page": "ec_demcurve_cond_dem",
      "title": "Create demand-incidence curves",
      "topics": [
        "ec_demcurve_cond_dem"
      ]
    },
    {
      "page": "ec_demcurve_inci",
      "title": "Create demand-incidence curves",
      "topics": [
        "ec_demcurve_inci"
      ]
    },
    {
      "page": "ec_draws_MU",
      "title": "Obtain MU_theta draws",
      "topics": [
        "ec_draws_MU"
      ]
    },
    {
      "page": "ec_draws_screen",
      "title": "Obtain Screening probability draws",
      "topics": [
        "ec_draws_screen"
      ]
    },
    {
      "page": "ec_estimates_MU",
      "title": "Obtain upper level model estimates",
      "topics": [
        "ec_estimates_MU"
      ]
    },
    {
      "page": "ec_estimates_screen",
      "title": "Summarize attribute-based screening parameters",
      "topics": [
        "ec_estimates_screen"
      ]
    },
    {
      "page": "ec_estimates_SIGMA",
      "title": "Obtain posterior mean estimates of upper level covariance",
      "topics": [
        "ec_estimates_SIGMA"
      ]
    },
    {
      "page": "ec_estimates_SIGMA_corr",
      "title": "Obtain posterior mean estimates of upper level correlations",
      "topics": [
        "ec_estimates_SIGMA_corr"
      ]
    },
    {
      "page": "ec_gen_err_ev1",
      "title": "Simulate error realization from EV1 distribution",
      "topics": [
        "ec_gen_err_ev1"
      ]
    },
    {
      "page": "ec_gen_err_normal",
      "title": "Simulate error realization from Normal distribution",
      "topics": [
        "ec_gen_err_normal"
      ]
    },
    {
      "page": "ec_lmd_NR",
      "title": "Obtain Log Marginal Density from draw objects",
      "topics": [
        "ec_lmd_NR"
      ]
    },
    {
      "page": "ec_lol_tidy1",
      "title": "Convert \"list of lists\" format to long \"tidy\" format",
      "topics": [
        "ec_lol_tidy1"
      ]
    },
    {
      "page": "ec_screen_summarise",
      "title": "Summarize posterior draws of screening",
      "topics": [
        "ec_screen_summarise",
        "ec_screen_summarize"
      ]
    },
    {
      "page": "ec_screenprob_sr",
      "title": "Screening probabilities of choice alternatives",
      "topics": [
        "ec_screenprob_sr"
      ]
    },
    {
      "page": "ec_summarize_attrlvls",
      "title": "Summarize attributes and levels",
      "topics": [
        "ec_summarise_attrlvls",
        "ec_summarize_attrlvls"
      ]
    },
    {
      "page": "ec_trace_MU",
      "title": "Generate MU_theta traceplot",
      "topics": [
        "ec_trace_MU"
      ]
    },
    {
      "page": "ec_trace_screen",
      "title": "Generate Screening probability traceplots",
      "topics": [
        "ec_trace_screen"
      ]
    },
    {
      "page": "ec_undummy",
      "title": "Converts a set of dummy variables into a single categorical variable",
      "topics": [
        "ec_undummy"
      ]
    },
    {
      "page": "ec_undummy_lowhigh",
      "title": "Convert dummy-coded variables to low/high factor",
      "topics": [
        "ec_undummy_lowhigh"
      ]
    },
    {
      "page": "ec_undummy_lowmediumhigh",
      "title": "Convert dummy-coded variables to low/medium/high factor",
      "topics": [
        "ec_undummy_lowmediumhigh"
      ]
    },
    {
      "page": "ec_undummy_yesno",
      "title": "Convert dummy-coded variables to yes/no factor",
      "topics": [
        "ec_undummy_yesno"
      ]
    },
    {
      "page": "ec_util_choice_to_long",
      "title": "Convert a vector of choices to long format",
      "topics": [
        "ec_util_choice_to_long"
      ]
    },
    {
      "page": "ec_util_dummy_mutualeclusive",
      "title": "Find mutually exclusive columns",
      "topics": [
        "ec_util_dummy_mutualeclusive"
      ]
    },
    {
      "page": "get_attr_lvl",
      "title": "Obtain attributes and levels from tidy choice data with dummies",
      "topics": [
        "get_attr_lvl"
      ]
    },
    {
      "page": "icecream",
      "title": "icecream",
      "topics": [
        "icecream"
      ]
    },
    {
      "page": "icecream_discrete",
      "title": "icecream_discrete",
      "topics": [
        "icecream_discrete"
      ]
    },
    {
      "page": "logMargDenNRu",
      "title": "Log Marginal Density (Newton-Raftery)",
      "topics": [
        "logMargDenNRu"
      ]
    },
    {
      "page": "pizza",
      "title": "pizza",
      "topics": [
        "pizza"
      ]
    },
    {
      "page": "prep_newprediction",
      "title": "Match factor levels between two datasets",
      "topics": [
        "prep_newprediction"
      ]
    },
    {
      "page": "vd_add_prodid",
      "title": "Add product id to demand draws",
      "topics": [
        "vd_add_prodid"
      ]
    },
    {
      "page": "vd_dem_summarise",
      "title": "Summarize posterior draws of demand (volumetric models only)",
      "topics": [
        "vd_dem_summarise",
        "vd_dem_summarize"
      ]
    },
    {
      "page": "vd_dem_vdm",
      "title": "Demand Prediction (Volumetric Demand Model)",
      "topics": [
        "vd_dem_vdm"
      ]
    },
    {
      "page": "vd_dem_vdm_screen",
      "title": "Demand Prediction (Volumetric demand, attribute-based screening)",
      "topics": [
        "vd_dem_vdm_screen"
      ]
    },
    {
      "page": "vd_dem_vdm_ss",
      "title": "Demand Prediction (Volumetric demand, accounting for set-size variation, EV1 errors)",
      "topics": [
        "vd_dem_vdm_ss"
      ]
    },
    {
      "page": "vd_est_vdm",
      "title": "Estimate volumetric demand model",
      "topics": [
        "vd_est_vdm"
      ]
    },
    {
      "page": "vd_est_vdm_screen",
      "title": "Estimate volumetric demand model with attribute-based conjunctive screening",
      "topics": [
        "vd_est_vdm_screen"
      ]
    },
    {
      "page": "vd_est_vdm_ss",
      "title": "Estimate volumetric demand model accounting for set size variation (1st order)",
      "topics": [
        "vd_est_vdm_ss"
      ]
    },
    {
      "page": "vd_LL_vdm",
      "title": "Log-Likelihood for compensatory volumetric demand model",
      "topics": [
        "vd_LL_vdm"
      ]
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