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ramchoice
View on CRAN: Click
here
Download and install ramchoice package within the R console
Install from CRAN:
install.packages("ramchoice")
Install from Github:
library("remotes")
install_github("cran/ramchoice") Install by package version:
library("remotes")
install_version("ramchoice", "3.0.0") Attach the package and use:
library("ramchoice")
Maintained by
Xinwei Ma
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-11-20
Latest Update: 2024-01-22
Description:
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) and Cattaneo, Cheung, Ma, and Masatlioglu (2022) , 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.
How to cite:
Xinwei Ma (2017). ramchoice: Revealed Preference and Attention Analysis in Random Limited Attention Models. R package version 3.0.0, https://cran.r-project.org/web/packages/ramchoice. Accessed 08 Oct. 2026.
Previous versions and publish date:
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Complete documentation for ramchoice
Functions, R codes and Examples using
the ramchoice R package
Some associated functions: genMat . logitAtte . logitSimu . print.ramchoiceRevealAtte . print.ramchoiceRevealPref . print.ramchoiceRevealPrefModel . rAtte . ramchoice-package . ramdata . revealAtte . revealPref . revealPrefModel . sumData . summary.ramchoiceRevealAtte . summary.ramchoiceRevealPref . summary.ramchoiceRevealPrefModel .
Some associated R codes: auxiliaryFunctions.R . ramchoice-package.R . ramdata.R . revealAtte.R . revealPref.R . revealPrefModel.R . Full ramchoice package functions and examples
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