logitr - Logit Models w/Preference & WTP Space Utility Parameterizations

Fast estimation of multinomial (MNL) and mixed logit (MXL) models in R. Models can be estimated using "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations. Weighted models can also be estimated. An option is available to run a parallelized multistart optimization loop with random starting points in each iteration, which is useful for non-convex problems like MXL models or models with WTP space utility parameterizations. The main optimization loop uses the 'nloptr' package to minimize the negative log-likelihood function. Additional functions are available for computing and comparing WTP from both preference space and WTP space models and for predicting expected choices and choice probabilities for sets of alternatives based on an estimated model. Mixed logit models can include uncorrelated or correlated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in Train (2009) <doi:10.1017/CBO9780511805271>. More details can be found in Helveston (2023) <doi:10.18637/jss.v105.i10>.

Last updated 3 days ago

log-likelihoodlogitlogit-modelmixed-logitmlogitmultinomial-regressionmxlmxl-modelspreference-spacepreferenceswillingness-to-paywtp

8.80 score 42 stars 1 packages 119 scripts 1.9k downloads

surveydown - Markdown-Based Surveys Using 'Quarto' and 'shiny'

Generate surveys using markdown and R code chunks. Surveys are composed of two files: a survey.qmd 'Quarto' file defining the survey content (pages, questions, etc), and an app.R file defining a 'shiny' app with global settings (libraries, database configuration, etc.) and server configuration options (e.g., conditional skipping / display, etc.). Survey data collected from respondents is stored in a 'PostgreSQL' database. Features include controls for conditional skip logic (skip to a page based on an answer to a question), conditional display logic (display a question based on an answer to a question), a customizable progress bar, and a wide variety of question types, including multiple choice (single choice and multiple choices), select, text, numeric, multiple choice buttons, text area, and dates.

Last updated 3 days ago

markdownquartoshinyshiny-appsshiny-rsupabasesurveysurveys

8.61 score 59 stars 84 scripts

cbcTools - Choice-Based Conjoint Experiment Design Generation and Power Evaluation in R

Design and evaluate choice-based conjoint survey experiments. Generate a variety of survey designs, including random designs, full factorial designs, orthogonal designs, D-optimal designs, and Bayesian D-efficient designs as well as designs with "no choice" options and "labeled" (also known as "alternative specific") designs. Conveniently inspect the design balance and overlap, and simulate choice data for a survey design either randomly or according to a multinomial or mixed logit utility model defined by user-provided prior parameters. Conduct a power analysis for a given survey design by estimating the same model on different subsets of the data to simulate different sample sizes. Full factorial and orthogonal designs are obtained using the 'DoE.base' package (Grömping, 2018) <doi:10.18637/jss.v085.i05>. D-optimal designs are obtained using the 'AlgDesign' package (Wheeler, 2022) <https://CRAN.R-project.org/package=AlgDesign>. Bayesian D-efficient designs are obtained using the 'idefix' package (Traets et al, 2020) <doi:10.18637/jss.v096.i03>. Choice simulation and model estimation in power analyses are handled using the 'logitr' package (Helveston, 2023) <doi:10.18637/jss.v105.i10>.

Last updated 9 months ago

cbcconjointdesigndiscrete-choicesawtoothsurvey

5.59 score 6 stars 65 scripts 420 downloads