hlmLab 0.2.0

Correctness

hlm_context() now computes the standard error of the contextual contrast from the full fixed-effect covariance matrix, Var(bB - bW) = Var(bB) + Var(bW) - 2 Cov(bB, bW), instead of assuming that the covariance is zero. In a random-intercept model with an exactly group-mean centered Level-1 predictor that covariance is zero by construction, so previously published random-intercept results are unchanged; it is generally nonzero when the model contains a random slope, when the predictor is centered in some other way, or when the Mundlak specification uses the raw predictor.
hlm_context() gains level and check_centering arguments, returns conf_low and conf_high columns, and stores the covariance of the two coefficients in the attribute "cov_within_between". A warning is issued when x_within does not appear to be group-mean centered, because the difference between the two coefficients is then not the contextual effect.
hlm_icc() warns when the model has more than one grouping factor, and when the first random-effect term contains a random slope, in which case the reported ICC is conditional on the Level-1 predictor being zero.

hlm_xint_geom() is deprecated. A random slope is unexplained heterogeneity in a Level-1 association; it is not a cross-level interaction, which requires an observed Level-2 moderator. The function remains available as an alias that warns and forwards to hlm_random_slope_plot(), and will be removed in a future release.

hlm_random_slope_plot() displays cluster-specific fitted lines from a random-slope model with the average line overlaid. Clusters are selected evenly across the distribution of conditional slopes by default, the plotting range is taken from the observed predictor, and remaining fixed effects are held at their sample means.
hlm_cross_level_plot() displays the Level-1 association at selected values of an observed Level-2 moderator, and warns when the model contains no such interaction.
hlm_icc_demo() simulates clustered data at several target intraclass correlations with the total variance held constant, so that students can see what low, moderate, and high ICC values look like. Clusters are ordered by their mean within each panel (sort_clusters = TRUE), so the sequence of cluster means is nearly flat at a low ICC and steep at a high one.
hlm_shrinkage_plot() compares raw cluster means with multilevel empirical Bayes estimates and draws an arrow between them, making partial pooling and its dependence on cluster size visible.

hlm_icc() accepts cluster_size = "auto", recovers cluster sizes from the fitted model, and reports an unequal-cluster-size design effect based on sum(n^2) / sum(n) alongside the usual mean-size approximation. The returned object gains deff_unequal, n_clusters, and mean_cluster_size.
hlm_icc_plot() labels the components with their percentages and reports the unequal-size design effect when available.
A testthat suite was added, including cases in which the within- and between-cluster estimates are deliberately correlated.
Examples for hlm_decompose() and hlm_decompose_long() are self-contained and run without external data.