Package: bpgmm 1.3.4

Fit a model | Summarize posterior samples | Common early mistakes | Naming convention | Citation

Observation model | PGMM covariance constraints | Priors and posterior updates | Augmented representation and sufficient statistics | [\sum_{i: z_i = k}(x_i - \tilde{\Lambda}k \tilde{y}{ki})(x_i - \tilde{\Lambda}k \tilde{y}{ki})' | Latent scores, means, and mixture weights | Hyperparameter updates | PGMM conditional posteriors (corrected forms) | RJMCMC moves | Birth and death | Split and combine | Covariance-structure moves | Interface mapping | Citation

Simulate data with informative and weak variables | Fit a short clustering chain | Rank variables by posterior allocation separation | Compare with loading magnitudes | Interpretation | Suggested reporting language

Matrix orientation | Check finite numeric inputs | Scale variables | Hyperprior defaults | Choose q_new | Choose m_range | Choose the starting covariance model | A prepared call | Practical checklist

Simulate a small clustering problem | Fit a fixed-model chain | Summarize posterior samples | Inspect component parameters | Scale the example to real data | What to report

Simulate clustered factor-analytic data | Run RJMCMC model selection | Compare true and posterior allocations | Interpreting short versus long runs

Simulate a diagnostic example | Run independent chains | Summarize each chain | Trace cluster counts and covariance models | Posterior co-clustering matrix | What to look for