lambdastar 0.8.1

Correct README links to files included in the source package and remove the private audit reference.
Exclude internal audit, validation and delivery reports and result tables from the CRAN archive. Retain user guides, runnable examples and package tests.
Remove documentation references to excluded reports. Statistical calculations and percentile semantics are unchanged.

lambdastar 0.8.0

Add lambda_panel() for fixed person-by-occasion models with parallel indicators, explicit observation maps, conditional term blocks and person-level case selection.
Implement default person-intercept adjustment by group demeaning; reuse the existing hypothesis, temperature and numerical F_H evaluator.
Bootstrap whole persons with distinct copy identities, rebuilding person-specific columns and degrees of freedom while preserving declared non-person encoding.
Deliver the derivation, an executable panel lesson and independent numerical, resampling, fixed-profile and compatibility evidence.
Enforce the exact saturation identity for captured energy. If boot::boot.ci returns no percentile endpoints, report quantile_unavailable without a crash or substituted limits. Ordinary percentile semantics remain unchanged.

Add lambda_reference() for a known scalar/vector origin and explicit retained mean direction, through the existing design, projection and F_H evaluator. Use d=N without nuisance, literal nuisance columns and uncentered encoded volume.
Add lambda_paired() for matching after-minus-before parallel measurement pairs. Estimate temperature from the differences and require T_q on their mean scale.
Carry known reference values with case/stratum/cluster draws; require a common origin/reference recipe for comparisons. Retain ordinary percentile semantics, zero/singular diagnostics and the existing centered/population behavior.
Include the derivation, a default-bootstrap lesson, independent fixed-profile and paired-covariance checks, plus regression evidence for the centered API.

Complete the centered model-space learning path with an explicit supported-scope table, conditional-reference interpretation, coding comparisons and finite-N population-target examples.
Include the E4 distribution/resource report, compact evidence and two synthetic datasets with a runnable ordinary-percentile lesson for regular and weak signal.
Make the model-space lessons reproducible under caller RNG and contrast options; regenerate help and deliver a checked source archive. Numerical algorithms, estimator selection, bootstrap defaults and endpoint rules are unchanged.

Add lambda_contrast() for named homogeneous coefficient restrictions L beta=0, using stable coefficient-space solves and the explicit restricted null model.
Add lambda_marginal_contrast() for predictor settings and signed averaging weights, with learned polynomial, spline and scale prediction coordinates.
Add encoding=“fixed” to design constructors; retain reevaluation as the default. Freeze raw encodings only after common selection, and rebuild full/null geometry in every case or cluster draw. Preserve declared L coding for F_H.
Allow comparisons of invertible contrast recodings with a common null reference. Diagnose unidentified full models, redundant restrictions and unsupported grid recipes. Keep percentile intervals, estimators and invalid-draw rules unchanged.
Include a runnable contrasts/bases lesson and independent restricted-fit, compatibility, resampling and portable-replay validation.

Add lambda_design() for portable, row-bound formula/lm/matrix specifications and lambda_hypothesis() for target terms conditional on explicit adjustment.
Preserve declared coding, factor contrasts, common case selection and existing bootstrap transformation policies. Reject stale inputs and incompatible model comparison reference recipes. Report conditional targets and omitted terms.
Reuse the measurement nuisance state and projected residual energy for F_H; share thermal calculations within comparisons. Keep the projection algorithms, estimators, percentile endpoints and undefined-draw rules unchanged.
Clarify that homogeneous isotropic measurement fluctuation does not require Gaussian responses or equal total group variances. The population estimator retains its separate assumptions. General coefficient restrictions and new reference/paired-mean spaces remain later increments.

Add an executable seven-chapter teaching example with known simulated signal, plots, prediction checkpoints, inspectable bootstrap draws and editable noise, item-count, sample-size and coding experiments. Preserve the compact quickstart.
Add separate population, nuisance, school-cluster and zero-case examples. Teaching runs preserve RNG state and save results only to a new/empty folder.
Include a worked default run and its figures in the installed documentation. Statistical estimators and numerical algorithms are unchanged from 0.4.0.

License the package under Apache 2.0 and use mike.hammes@mikehammes.name as the author and maintainer contact.
Make estimator="paper" the default throughout points, returned lambda/kappa fields, summaries and bootstrap draws. Keep estimator="population" explicit; estimates and inference identify the selected calculation. This changes the default bootstrap statistic from 0.3.1; choose population to retain that method.
Add shared nuisance formula/matrix adjustment to measurement, model and comparison APIs, using N-rank(B) and refitting nuisance effects on every draw.
Add whole-cluster resampling within strata, variable draw sizes and optional row-index lists. Add explicit type-7 percentile interpolation for paper replay.
Retain ordinary percentile intervals, reduced contrast coding, undefined-value diagnostics and caller RNG restoration. No CI endpoint alterations.
Add independent conditional-moment, cluster and estimator checks, plus public API reproduction of the OECD manuscript analysis and random-design validation.

Preserve explicit factor contrast matrices, including fewer than G-1 columns, when rebuilding bootstrap models. Formula transformations, saved lm models and comparisons use the supplied coding without adding contrast directions.
Add regression tests against explicit design matrices and independent OLS for reduced contrasts, transformations and interactions.

All bootstrap confidence intervals use direct percentiles of the parameter draws, computed by boot::boot.ci(type=“perc”). Defaults: B=5000, 95%, seed=1.
Population point estimation is independent of confidence intervals and is available also with bootstrap=FALSE. The same estimator runs on every draw.
Undefined kappa, singular temperature and model failures are counted explicitly. Missing draws suppress the affected regular interval; valid zero and one values stay in their distributions.
Optional conditional_quantiles are a separate descriptive output. They never substitute for a regular interval.
ci.method accepts only “percentile”, also for temperature intervals.
Existing finite-person numerical estimates and F_H calculations are preserved.

Locally installable measurement core, model adapters, numerical F_H and common-data comparison wrapper, with executable examples and validation.

Measurement/projection core, numerical capacity, temperature integrals, encoded geometry and parsimony functional.