Package: ramchoice 2.2

ramchoice: Revealed Preference and Attention Analysis in Random Limited Attention Models

It is widely documented in psychology, economics and other disciplines that socio-economic agent may not pay full attention to all available alternatives, rendering standard revealed preference theory invalid. This package implements the estimation and inference procedures of Cattaneo, Ma, Masatlioglu and Suleymanov (2020) <arxiv:1712.03448> and Cattaneo, Cheung, Ma, and Masatlioglu (2022) <arxiv:2110.10650>, which utilizes standard choice data to partially identify and estimate a decision maker's preference and attention. For inference, several simulation-based critical values are provided.

Authors:Matias D. Cattaneo, Paul Cheung, Xinwei Ma, Yusufcan Masatlioglu, Elchin Suleymanov

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ramchoice.pdf |ramchoice.html
ramchoice/json (API)

# Install 'ramchoice' in R:
install.packages('ramchoice', repos = c('https://xinweima.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • ramdata - Ramdata: Simulated Choice Data

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

8 exports 0.09 score 1 dependencies 3 scripts 385 downloads

Last updated 8 months agofrom:e882a9e7a0. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 20 2024
R-4.5-winOKAug 20 2024
R-4.5-linuxOKAug 20 2024
R-4.4-winOKAug 20 2024
R-4.4-macOKAug 20 2024
R-4.3-winOKAug 20 2024
R-4.3-macOKAug 20 2024

Exports:genMatlogitAttelogitSimurAtterevealAtterevealPrefrevealPrefModelsumData

Dependencies:MASS