RobustLPA: Robust Latent Profile Analysis
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. The EM engine
implements a Full Information Maximum Likelihood (FIML) approach, Huber
weighting, and LASSO regularization with k-fold cross-validation for optimal
penalty tuning. The MCMC engine utilizes a Bayesian Lasso approach with
Laplace priors, the same Huber down-weighting available in the EM engine,
multiple chains (4 by default), and classic Gelman-Rubin/effective sample
size convergence diagnostics. It supports multiple geometric variance-covariance
models, along with functions for bootstrapped likelihood ratio tests (BLRT), BCH
auxiliary variable analysis, and plotting.
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>.
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