Recovering a Basic Space from Issue Scales
Aldrich-McKelvey Scaling
Blackbox transpose Scaling
Blackbox Scaling
Bootstrap of Aldrich-McKelvey Scaling
Bootstrap of Blackbox Transpose Scaling
Extraction function for fit of scaling model
Extraction function for scaled individuals
Aldrich-McKelvey Negative Coordinate Distribution Plot
Aldrich-McKelvey Positive Coordinate Distribution Plot
Aldrich-McKelvey Coordinate Distribution Plot
Aldrich-McKelvey Coordinate Distribution Plot
Blackbox Coordinate Distribution Plot
Blackbox Transpose Coordinate Distribution Plot
Bootstrapped Aldrich-McKelvey Stimulus Plots
Bootstrapped Blackbox Transpose Stimulus Plots
Aldrich-McKelvey Coordinate Cumulative Distribution Plot
Blackbox Transpose Coordinate Cumulative Distribution Plot
Predict method of aldmck objects
Predict method of blackbox objects
Predict method of blackbt objects
Stimulus extraction function
Aldrich-McKelvey Summary
Blackbox Summary
Blackbox-Transpose Summary
Provides functions to estimate latent dimensions of choice and judgment using Aldrich-McKelvey and Blackbox scaling methods, as described in Poole et al. (2016, <doi:10.18637/jss.v069.i07>). These techniques allow researchers (particularly those analyzing political attitudes, public opinion, and legislative behavior) to recover spatial estimates of political actors' ideal points and stimuli from issue scale data, accounting for perceptual bias, multidimensional spaces, and missing data. The package uses singular value decomposition and alternating least squares (ALS) procedures to scale self-placement and perceptual data into a common latent space for the analysis of ideological or evaluative dimensions. Functionality also include tools for assessing model fit, handling complex survey data structures, and reproducing simulated datasets for methodological validation.