{ "_id": "6ae20c22327abcdf86865458", "Package": "RobustLPA", "Title": "Robust Latent Profile Analysis", "Version": "1.1.0", "Authors@R": "person(\"Valerio Riccardo\", \"Aquila\", role = c(\"aut\", \"cre\"),\nemail = \"valerio_aquila@hotmail.it\", comment = c(ORCID = \"0009-0004-2231-2141\"))", "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', it reliably extracts latent\nprofiles using both Expectation-Maximization (EM) and Markov\nChain Monte Carlo (MCMC) Bayesian estimation. Robustness is\nobtained either by Huber-type down-weighting or by mixtures of\nmultivariate t distributions (a likelihood-based robust model,\nsee Peel and McLachlan (2000) ).\nMissing data are handled by full information maximum likelihood\nwith the exact EM treatment of incomplete observations (data\naugmentation in the MCMC engine). The EM engine also supports\nLASSO regularization with k-fold cross-validation for penalty\ntuning; the MCMC engine uses a Bayesian Lasso with Laplace\npriors, multiple chains, Gelman-Rubin/effective sample size\ndiagnostics and the widely applicable information criterion. It\nsupports six geometric variance-covariance models, along with\nfunctions for bootstrapped likelihood ratio tests (BLRT), BCH\nauxiliary variable analysis, and plotting. For longitudinal\ndata, it fits robust growth mixture models and latent class\ngrowth analysis (Muthen and Shedden (1999)\n) for one or several\noutcomes measured on unbalanced occasions, with Gaussian,\nHuber-weighted or multivariate-t (Pinheiro, Liu and Wu (2001)\n) latent classes, by EM and\nMCMC, and optional adaptive LASSO penalties (Zou (2006)\n) that identify stable\ntrajectories and the outcomes that differentiate the classes.\nFor methodological details on the Bootstrapped Likelihood Ratio\nTest, see Nylund et al. (2007) .\nFor robust clustering methods, see Garcia-Escudero et al.\n(2010) . For BCH auxiliary\nvariable analysis, see Bolck et al. (2004)\n.", "License": "GPL (>= 3)", "Encoding": "UTF-8", "Config/testthat/edition": "3", "VignetteBuilder": "knitr", "LazyData": "true", "NeedsCompilation": "yes", "Config/roxygen2/version": "8.0.0", "Packaged": { "Date": "2026-10-28 07:15:33 UTC", "User": "root" }, "Author": "Valerio Riccardo Aquila [aut, cre] (ORCID:\n)", "Maintainer": "Valerio Riccardo Aquila ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-09-27 22:50:15 UTC", "RemoteUrl": "https://github.com/cran/RobustLPA", "RemoteRef": "HEAD", "RemoteSha": "f818f52a1d9f00f65fd46b9d8981b4b355587a54", "_user": "cran", "_type": "src", "_file": "RobustLPA_1.1.0.tar.gz", "_fileid": "https://r2.ropensci.org/be51db5807862b476abbd6dac6c70d5dd9f4fecbee2d5af7e349fdd206fa1518", "_filesize": 1512692, "_sha256": "be51db5807862b476abbd6dac6c70d5dd9f4fecbee2d5af7e349fdd206fa1518", "_expires": "2027-02-05T07:28:53.000Z", "_created": "2026-10-28T07:15:33.000Z", "_published": "2026-10-28T14:52:18.087Z", "_upstream": 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