basicspace - Recovering a Basic Space from Issue Scales
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.
