intraclass 0.1.0

First public release.

intraclass estimates interrater-reliability intraclass correlation coefficients (ICCs) within the generalizability-theory framework. Variance components come from a linear mixed model rather than from classical ANOVA mean squares. A point estimate is never reported without an interval, and ci_method selects which interval that is. The package requires R 4.5.0 or newer.

What ships

• icc() fits the model and reports the ICC family the design defines: absolute agreement or consistency (type), single or average (unit), random or fixed raters (raters), one-way or two-way (model). See Getting started and ?icc.
• Data need not form a complete, balanced grid. There is support for imbalanced, incomplete, and multilevel (nested) designs: unequal ratings per subject, missing ratings, and subjects nested in a higher-level unit such as a classroom or clinic. A cluster column on a two-way design switches on the multilevel ICC, adding a cluster level when the same raters span every cluster. See Multilevel designs: subject and cluster level and ?icc for the layouts and where it refuses.
• Rating a subject-by-rater cell more than once gives a within-cell replicate design. On a two-way random design icc() splits the single-rating residual into a subject-by-rater interaction and pure error, and occasions reports the reliability of one rating or, on balanced replicates, of the mean. See D-studies and within-cell replicates and ?icc for the designs replicates support and where they refuse.
• d_study() projects a fitted reliability to other numbers of raters (m) or occasions (n_o), with a plot() and ggplot2::autoplot() curve. D-studies and within-cell replicates gives the designs each projection supports and where it refuses.
• choose_icc() recommends which coefficient or coefficients to report, explains the reasoning, and prints the icc() call to run. Its type, unit and level questions each take "both", which asks for the pair rather than making you choose one. It gives advice only. See Choosing an ICC.
• tidy() and glance() return tidy summaries of a fit or a projection. print(), format(), plot() and ggplot2::autoplot() methods are provided for both classes, and summary() for an icc() fit. ggplot2 is a Suggests dependency.
• Datasets ratings and ratings_incomplete, used throughout the documentation.
• Eight articles: Getting started, Choosing an ICC, Multilevel designs: subject and cluster level, Estimation engines, Confidence-interval methods, D-studies and within-cell replicates, Glossary, and Comparison with other packages.

Engines

• The default engine is glmmTMB, the package’s only hard engine dependency. engine = "lme4", engine = "lavaan" and engine = "brms" are selectable. Which designs each engine covers, and where it refuses, is documented in Estimation engines and ?icc.
• The brms engine fits both random- and fixed-rater models under a sourced half-t(4, 0, 1) prior on every random-effect standard deviation. Its point estimate is the posterior mode and its interval a percentile credible interval, so ci_method = "posterior" is forced. Supplying a custom prior is a deliberate deviation: icc() warns, and the coverage results this package reports no longer apply.
• The lme4 package itself is already on your library path after a plain install, because glmmTMB lists it in its own Imports, but the lme4 engine also needs merDeriv, which does not arrive. merDeriv, lavaan and brms sit in this package’s Suggests, and a plain install fetches none of the three.

Confidence intervals

• ci_method selects the interval: "montecarlo" (the default), "bootstrap", "npbootstrap", "searle", "burch", "mpl", and "posterior" under brms. Where a method does not apply to the design, the call aborts with a classed error. Confidence-interval methods compares them; ?icc gives the per-method conditions.
• Interval coverage was studied by simulation before release, and the limitations those studies found are documented rather than smoothed over. The Monte-Carlo default under-covers when the subject effects are strongly skewed or heavy-tailed, worst 0.6725 at chi-square(1) subject effects with a true ICC of 0.6, 50 subjects and 5 raters. "burch" is no remedy there (worst 0.6655).
• "searle" and "burch" are the two classical closed forms, and the three grids below measure their widths: the smaller grid’s 16 cells and the larger grid’s 64 cells draw only the subject effects from the non-normal family. The third draws the residual from the same family as the subject effect.
• Which of the two closed forms gives the narrower interval is conditional. On both grids that vary only the subject effect, "burch" is the narrower of the two in 16 of 16 cells of the smaller grid and 59 of 64 cells of the larger grid.
• That width margin holds much the same up to a true ICC of 0.3 rather than shrinking as the true ICC rises (on the larger grid; the smaller grid’s margin does shrink across its levels). The "burch" width advantage then collapses to near parity at a true ICC of 0.6, on the one grid reaching that value. It also shrinks steadily as the subject count grows, measured at 5 raters.
• What "burch" does against "searle" also depends on the residual, and the three grids measure that: the two grids that vary only the subject effect put it narrower nearly everywhere, while the third, which draws the residual from the same family as the subject effect, puts it wider at every symmetric heavy-tailed family measured (a median width ratio of 1.2963 at t(5) with 100 subjects) and narrower at every lighter-tailed one, the normal included.
• The ci_method = "mpl" documentation states the interpolation evidence behind off-node subject counts. The correction constant is calibrated at subject-count nodes and linearly interpolated between them. The interpolated path is coverage-validated at each supported confidence level, at the default 0.95 by three off-node cells, each clearing its pre-registered coverage floor. The documentation also says what that validation does not establish (interpolated values are validated at a handful of geometries, not calibrated, and the interval’s asymmetry direction is not uniform across rater counts). Every universal or negative claim this documentation makes about the validated cells is settled mechanically, in CI, against the committed coverage fixture (data-raw/check-mpl-doc-claims.py).

When a call fails

• Errors are classed and name the quantities that failed the check, rather than asserting a cause. Where an interval method aborts on degenerate data, the error names another ci_method only after running it on your data and confirming it returns a usable interval.