statMatchLCM: Statistical Matching Using Latent Class Models
Provides a revised framework (1.2->2.0) for statistical matching of categorical data based on latent class models, including multiple imputation, predictive modeling, nearest-neighbour hot-deck matching, and probabilistic assignment of categorical target variables. It allows researchers to perform data integration when no unique identifiers are available by modeling the joint distribution of variables through latent categorical structures. The package supports estimation of latent class models, probabilistic matching between donor and recipient data sets, and generation of synthetic linked data under uncertainty. It is particularly useful in survey research and data fusion applications where combining information from multiple sources is required while preserving statistical properties and accounting for measurement error and missing data mechanisms.
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