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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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