Hierarchical Bayesian Aldrich-McKelvey Scaling via 'Stan'
Fit an FBAM model using optimization
Extract point estimates or other summaries of marginal posterior distr...
Extract data for plotting results from an HBAM model
Perform K-fold cross-validation
Fit an HBAM model
Hierarchical Bayesian Aldrich-McKelvey Scaling via Stan
Plot posterior densities of parameter averages by group
Plot individual parameter estimates over self-placements
Plot estimated respondent positions
Plot estimated stimulus positions
Prepare data for a K-fold cross-validation of an HBAM model
Prepare data to fit an HBAM or FBAM model
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 Bølstad (2024) <doi:10.1017/pan.2023.18>.