NEWS

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lambdastar 0.8.2 (2026-10-11)

• Compute indicator disagreement before nuisance projection and row averaging. Identical indicators now retain exact zero temperature on Apple Silicon, including platforms without extended-precision row sums. Singular estimates remain unavailable and bootstrap inference is skipped as intended.
• Preserve genuinely small positive temperatures without a clipping tolerance, and improve fluctuation accuracy in the presence of large shared profiles.
• Add regression checks for double-precision averaging, independent pairwise differences, indicator order, fixed references and response scaling.
• Add GitHub Actions checks using native ARM64 R on macOS and R on Linux and Windows, with downloadable check logs and platform details.

lambdastar 0.8.1 (2026-10-07)

• 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.

lambdastar 0.7.0

• 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.

lambdastar 0.6.1

• 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.

lambdastar 0.6.0

• 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.

lambdastar 0.5.0

• 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.

lambdastar 0.4.1

• 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.

lambdastar 0.4.0

• License the package under Apache 2.0 and use [email protected] 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.

lambdastar 0.3.1

• 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.

lambdastar 0.3.0

• 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.

lambdastar 0.1.0

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

lambdastar 0.0.3

• Person-bootstrap infrastructure, RNG restoration and diagnostics.

lambdastar 0.0.2

• Model adapters, common-case comparison and S3 summaries.

lambdastar 0.0.1

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