Package: RobustLPA
Title: Robust Latent Profile Analysis
Version: 1.1.0
Authors@R: 
    person("Valerio Riccardo", "Aquila", role = c("aut", "cre"),
    email = "valerio_aquila@hotmail.it", comment = c(ORCID = "0009-0004-2231-2141"))
Description: Provides a comprehensive toolset for estimating Latent
        Profile Analysis (LPA) models that are robust to multivariate
        outliers and missing data. By integrating a high-performance
        'C++' engine via 'RcppArmadillo', it reliably extracts latent
        profiles using both Expectation-Maximization (EM) and Markov
        Chain Monte Carlo (MCMC) Bayesian estimation. Robustness is
        obtained either by Huber-type down-weighting or by mixtures of
        multivariate t distributions (a likelihood-based robust model,
        see Peel and McLachlan (2000) <doi:10.1023/A:1008981510081>).
        Missing data are handled by full information maximum likelihood
        with the exact EM treatment of incomplete observations (data
        augmentation in the MCMC engine). The EM engine also supports
        LASSO regularization with k-fold cross-validation for penalty
        tuning; the MCMC engine uses a Bayesian Lasso with Laplace
        priors, multiple chains, Gelman-Rubin/effective sample size
        diagnostics and the widely applicable information criterion. It
        supports six geometric variance-covariance models, along with
        functions for bootstrapped likelihood ratio tests (BLRT), BCH
        auxiliary variable analysis, and plotting. For longitudinal
        data, it fits robust growth mixture models and latent class
        growth analysis (Muthen and Shedden (1999)
        <doi:10.1111/j.0006-341X.1999.00463.x>) for one or several
        outcomes measured on unbalanced occasions, with Gaussian,
        Huber-weighted or multivariate-t (Pinheiro, Liu and Wu (2001)
        <doi:10.1198/10618600152628059>) latent classes, by EM and
        MCMC, and optional adaptive LASSO penalties (Zou (2006)
        <doi:10.1198/016214506000000735>) that identify stable
        trajectories and the outcomes that differentiate the classes.
        For methodological details on the Bootstrapped Likelihood Ratio
        Test, see Nylund et al. (2007) <doi:10.1080/10705510701575396>.
        For robust clustering methods, see Garcia-Escudero et al.
        (2010) <doi:10.1007/s11634-010-0064-5>. For BCH auxiliary
        variable analysis, see Bolck et al. (2004)
        <doi:10.1093/pan/mph001>.
License: GPL (>= 3)
Encoding: UTF-8
LinkingTo: Rcpp, RcppArmadillo
Imports: Rcpp, ggplot2, stats, utils, bayesplot, coda
Suggests: parallel, knitr, rmarkdown, testthat (>= 3.1.5), lme4, nlme
Config/testthat/edition: 3
VignetteBuilder: knitr
Depends: R (>= 3.6)
LazyData: true
NeedsCompilation: yes
Config/roxygen2/version: 8.0.0
Packaged: 2026-10-28 07:20:58 UTC; root
Author: Valerio Riccardo Aquila [aut, cre] (ORCID:
        <https://orcid.org/0009-0004-2231-2141>)
Maintainer: Valerio Riccardo Aquila <valerio_aquila@hotmail.it>
Config/pak/sysreqs: libicu-dev
Repository: https://vraquila.r-universe.dev
Date/Publication: 2026-09-27 22:50:15 UTC
RemoteUrl: https://github.com/cran/RobustLPA
RemoteRef: HEAD
RemoteSha: f818f52a1d9f00f65fd46b9d8981b4b355587a54