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: 2026-09-27 20:01:23 UTC; hp
Author: Valerio Riccardo Aquila [aut, cre] (ORCID:
    <https://orcid.org/0009-0004-2231-2141>)
Maintainer: Valerio Riccardo Aquila <valerio_aquila@hotmail.it>
Repository: CRAN
Date/Publication: 2026-09-27 22:50:15 UTC
