RobustLPA 1.0.0

Major release: a new Bayesian MCMC estimation engine alongside the existing EM engine, an optional classical (non-robust) estimation mode, LASSO regularization, a new BCH auxiliary-variable method, parallel computing throughout, S3 print/summary methods, a new bundled dataset, and a new package vignette.

• New MCMC engine. robust_lpa(engine = "MCMC") estimates the same six variance-covariance parameterizations via Gibbs sampling, with a Bayesian Lasso (Laplace-prior) option for the profile means (prior_laplace) and the same Huber-weighted robust/classical toggle as the EM engine (robust/alpha). Runs multiple independent chains by default (n_chains = 4), aligns each chain’s arbitrarily-ordered profile labels to a common ordering before pooling, and reports classic Gelman-Rubin and effective sample size convergence diagnostics ($mcmc_diagnostics, via the new coda dependency) for every scalar parameter, warning if any exceeds the standard 1.1 R-hat threshold. plot_mcmc_chains() draws multi-chain trace plots via bayesplot.
• Optional robustness. robust_lpa() gains a robust argument (default TRUE) and alpha, so either engine can be run with Huber-weighted robust estimation (as in 0.1.0) or with classical (non-robust) maximum likelihood, using the same engine, missing-data handling, and variance- covariance parameterizations either way.
• LASSO regularization. The EM engine supports LASSO-type soft- thresholding shrinkage of the profile means (lambda), and estimate_profiles_robust(tune_lasso = TRUE) selects lambda by k-fold cross-validation (k_folds, lambda_grid).
• Parallel computing. A cores argument runs independent work in parallel throughout the package: EM random restarts or MCMC chains within a single robust_lpa() call; the model/profile grid (and cross-validation folds) in estimate_profiles_robust(); bootstrap replicates in blrt_robust(); and bootstrap correction replicates in bch_robust(). Uses parallel::mclapply() on macOS/Linux and a parallel::makeCluster() PSOCK backend on Windows.
• BCH auxiliary-variable analysis. New bch_robust() implements the Bolck-Croon-Hagenaars (2004) three-step method for relating fitted profiles to a continuous auxiliary/distal outcome variable, correcting for classification error. Optionally (correction = "bootstrap") adds a nonparametric bootstrap correction for classification uncertainty in the step-1 model, reporting bootstrap standard errors, confidence intervals, and a Wald chi-square test for the profile means ($Bootstrap_Correction) – a practical approximation to the Bakk, Oberski & Vermunt (2014) sandwich correction, preferable to the base $ANOVA_Table F-test (which treats the classification matrix as fixed/known) for publication-grade inference.
• Enhanced bootstrapped likelihood ratio test. blrt_robust() (present since 0.1.0) now supports both estimation engines (engine = "EM" or "MCMC"), replicates the observed data’s FIML missingness pattern in every simulated bootstrap sample, and runs its bootstrap replicates in parallel via cores.
• S3 classes for fitted models. robust_lpa() now returns an object of class "robust_lpa" with print() and summary() methods: print() is a compact overview (engine, model, profile count, N, headline fit indices, mixing proportions), and summary() adds per-profile means, profile sizes, and, for the MCMC engine, the Gelman-Rubin/effective sample size range – both focused on what’s needed for a first read, rather than dumping the full fitted object.
• Refit-ready fitted objects. robust_lpa() fits now store $data (the numeric matrix used for estimation) and $call_args (every argument controlling the fit), so other functions can refit the identical specification on new or resampled data; used internally by bch_robust()’s bootstrap correction.
• Regularization safety net for covariance estimation. Variance- covariance models that allow off-diagonal terms (models 3-6) use a scale-relative eigenvalue floor to keep every estimated covariance matrix numerically positive-definite, without perturbing already well-behaved matrices. Fits (from either engine) whose smallest profile holds under 2% of observations emit a transparency warning identifying the model, profile count, and engine, since this pattern can indicate an unusually small genuine subgroup or an unconstrained-covariance model over-fitting a handful of near-coincident points – worth a second look either way (e.g. a more constrained model, more n_starts, or fewer profiles).
• New bundled dataset. neuro_data (n = 250, two groups) is a new synthetic dataset with a genuine group-level difference in covariance structure (not only in means) and calibrated, variable-magnitude outlier contamination, so that model/profile selection, robust-vs-classical comparisons, and classification recovery all behave as a realistic worked example throughout the package’s documentation. See ?neuro_data for details and data-raw/generate_neuro_data.R for the full generative code.
• Package vignette. A new vignette (vignette("RobustLPA")) walks through the full workflow: choosing a variance-covariance model, EM vs. MCMC estimation, robust vs. classical estimation, LASSO regularization, model/profile selection, the bootstrapped likelihood ratio test, and BCH auxiliary-variable analysis, on neuro_data.
• Packaging: declared the parallel dependency under Suggests; reference DOIs throughout the documentation use the \doi{} Rd macro, as recommended by CRAN.

RobustLPA 0.1.0

• Initial CRAN submission.
• Added robust Latent Profile Analysis (LPA) estimation using Huber weighting to handle multivariate outliers.
• Implemented Full Information Maximum Likelihood (FIML) via a high-performance C++ engine (RcppArmadillo) to natively handle missing data.
• Included multiple geometric variance-covariance model parameterizations (Models 1 to 6).
• Provided blrt_robust() for Bootstrapped Likelihood Ratio Tests.
• Provided plot_robust_lpa() for publication-ready visualizations using ggplot2.