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  "Title": "Recovering a Basic Space from Issue Scales",
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  "Date": "2024-12-02",
  "Author": "Royce Carroll [aut], Christopher Hare [aut, cre], Jeffrey B.\nLewis [aut], James Lo [aut], Keith T. Poole [aut], Howard\nRosenthal [aut]",
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  "Description": "Provides functions to estimate latent dimensions of choice\nand judgment using Aldrich-McKelvey and Blackbox scaling\nmethods, as described in Poole et al. (2016,\n<doi:10.18637/jss.v069.i07>). These techniques allow\nresearchers (particularly those analyzing political attitudes,\npublic opinion, and legislative behavior) to recover spatial\nestimates of political actors' ideal points and stimuli from\nissue scale data, accounting for perceptual bias,\nmultidimensional spaces, and missing data. The package uses\nsingular value decomposition and alternating least squares\n(ALS) procedures to scale self-placement and perceptual data\ninto a common latent space for the analysis of ideological or\nevaluative dimensions. Functionality also include tools for\nassessing model fit, handling complex survey data structures,\nand reproducing simulated datasets for methodological\nvalidation.",
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