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hbamr  

Hierarchical Bayesian Aldrich-McKelvey Scaling via 'Stan'
View on CRAN: Click here


Download and install hbamr package within the R console
Install from CRAN:
install.packages("hbamr")

Install from Github:
library("remotes")
install_github("cran/hbamr")

Install by package version:
library("remotes")
install_version("hbamr", "2.4.4")



Attach the package and use:
library("hbamr")
Maintained by
Jørgen Bølstad
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-03-14
Latest Update: 2025-03-05
Description:
Perform hierarchical Bayesian Aldrich-McKelvey scaling using Hamiltonian Monte Carlo via 'Stan'. Aldrich-McKelvey ('AM') scaling is a method for estimating the ideological positions of survey respondents and political actors on a common scale using positional survey data. The hierarchical versions of the Bayesian 'AM' model included in this package outperform other versions both in terms of yielding meaningful posterior distributions for respondent positions and in terms of recovering true respondent positions in simulations. The package contains functions for preparing data, fitting models, extracting estimates, plotting key results, and comparing models using cross-validation. The original version of the default model is described in B
How to cite:
Jørgen Bølstad (2023). hbamr: Hierarchical Bayesian Aldrich-McKelvey Scaling via 'Stan'. R package version 2.4.4, https://cran.r-project.org/web/packages/hbamr. Accessed 04 Jun. 2026.
Previous versions and publish date:
1.0.5 (2023-03-14 19:30), 1.1.1 (2023-04-24 21:20), 1.1.3 (2023-06-07 16:20), 1.1.6 (2023-06-09 18:10), 1.2.0 (2023-09-25 17:00), 2.0.1 (2024-01-08 12:40), 2.1.0 (2024-01-17 16:10), 2.1.1 (2024-01-25 11:30), 2.1.2 (2024-02-07 10:20), 2.2.0 (2024-02-14 15:40), 2.2.1 (2024-02-23 13:30), 2.3.0 (2024-03-31 21:20), 2.3.1 (2024-06-04 17:50), 2.3.2 (2024-09-23 14:20), 2.4.0 (2025-01-27 00:30), 2.4.1 (2025-02-13 06:30), 2.4.2 (2025-03-05 12:40), 2.4.4 (2025-08-18 13:00), 2.4.5 (2026-02-14 23:10)
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Complete documentation for hbamr
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