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  "Description": "Provides a comprehensive toolset for estimating Latent\nProfile Analysis (LPA) models that are robust to multivariate\noutliers and missing data. By integrating a high-performance\n'C++' engine via 'RcppArmadillo' with a Full Information\nMaximum Likelihood (FIML) approach and Huber weighting, it\nreliably extracts latent profiles even in complex datasets. It\nsupports multiple geometric variance-covariance models, along\nwith functions for bootstrapped likelihood ratio tests and\nplotting. For methodological details on the Bootstrapped\nLikelihood Ratio Test, see Nylund et al. (2007)\n<doi:10.1080/10705510701575396>. For robust clustering methods,\nsee Garcia-Escudero et al. (2010)\n<doi:10.1007/s11634-010-0064-5>.",
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