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Gtheory4LLM 0.5.0 — Study Planning

Source version: 0.5.0. For versioned archives, manuals and publication status, see the repository manifest and GitHub releases. These repository records are excluded from the package archive; this source version does not assert that a corresponding release has been published.

This version adds bounded study-planning tools. Historical release assets and the numerical acceptance rules are unchanged.

• gt_batch_reliability() and gt_batch_dstudy() project item-score, named contrast and aggregate error under a fitted equal fixed Gaussian batching layout. Targets are conditional on that layout; no new-batch-size effect or universal batch-adjusted G is inferred.
• gt_plan() enumerates supplied allocation grids and ranks them using dated, user-supplied cost assumptions, constraints and point or available lower-bound targets. It retains infeasible candidates, Pareto options and baseline marginal benefits without changing the declared score universe.
• gt_simulate() generates balanced Gaussian studies and gt_pilot_plan() refits prospective pilot designs, retaining all attempts, numerical refusals, interval availability and Monte Carlo uncertainty. These are model-based design simulations, not calibrated bootstrap intervals or coverage guarantees.
• Batch D-study data frames retain allocation counts and essential interpretation fields in ordinary columns, so CSV exports remain interpretable without R attributes. Pilot replicate ledgers retain numerical acceptance-failure codes and the selected refit attempt without retaining whole fit objects.
• Batch projections preserve distinct numeric item identifiers, including large adjacent values that have the same default printed representation. Use the returned layout$item keys to name contrast and aggregate weights. This corrects a false refusal after an otherwise valid fit; covariance estimation and projection arithmetic are unchanged.

Gtheory4LLM 0.4.1

0.4.0 was tagged and its bundle staged, but it was not published or submitted: its release check on R built without long double refused a fit on one machine and the test could not say so. 0.4.1 is 0.4.0 with that test corrected and no other change to the package.

• tests/package-characterization.R required every discrete case to be numerically accepted. On some machines the discrete optimizer reports completion without reaching a stationary point (#59), the engine refuses the fit through its stationarity and restart-stability safeguards, and the test then stopped at the coefficients it could not compute. The ordinal case, the one case observed to be refused this way, is now reported as that platform’s outcome when it is refused by those two safeguards and by nothing else: its estimates are not compared there, but it must still carry every other recorded field, the same model terms and the same variance names. Any other case refused this way, a refusal for any other reason, or accepted numbers that moved still fail. The acceptance and completion flags are recorded as the engine set them, and a fit without a single TRUE or FALSE flag stops the test. The comparison and the recording are exercised with constructed records and fits before any fit is compared. The engine is unchanged.

Gtheory4LLM 0.4.0

Changes since 0.2.0. The development line was versioned 0.3.0.9000 and then 0.4.0.9000; no 0.3.0 was released. For a design without a batch declaration, every estimate, coefficient and acceptance decision in tests/package-characterization.R reproduces its values: the fitting arithmetic is untouched, and the changes add refusals, evidence and reporting. The one new model is the shared call effect a Gaussian fit estimates for a design that declares equal fixed batches, described first below.

Check on R built without long double

• CRAN’s check of 0.2.0 on R built without long double stopped in tests/package-discrete-safety.R, where one ordinal fit on a boundary panel was required to be numerically accepted. On that build the optimizer reports completion without leaving its starting values, and the fit is refused by the stationarity check. Refusing it is the correct outcome; requiring acceptance was the test’s error.
• The test still asserts on every platform what it exists to guard: no evaluation at the variance boundary becomes an attempt error, and the fit uses variance coordinates. Accepted fits must reproduce the reference likelihood and variance components and yield a finite latent coefficient. Other fits must be explicitly rejected, with a nonempty set of reasons consisting only of the stationarity or restart/tolerance-stability safeguards. Movement away from the starting values does not establish numerical acceptance. Constructed records exercise both outcomes and reject missing acceptance flags, empty or unexpected reasons, boundary errors and accepted records with incorrect reference estimates.
• The optimizer is unchanged. That its search can stall in this way, when an unusable evaluation’s penalty value enters a finite-difference gradient, is a limitation of the discrete engine and is not addressed by this release; it is tracked in issue #59.

Declared batches

• gt_batch() declares that items were annotated several to a call, and gt_design() carries the declaration to every function that receives the design or a fit. With none declared, items are independent given the declared sources and every result is what it was. By default a declaration assumes items in one call affect each other equally; sequential and neighbor, both off by default, declare that an item may be affected by a stated number of preceding items or of items on either side, at most one less than the batch size. by names a facet, such as the evaluator, across whose levels the dependence may differ.
• Which items shared a call is inferred or recorded. Without id, batches are cut from the item order and are the same in every condition; that is a reconstruction, and it cannot detect items removed after collection. With id naming a column that identifies each row’s call, the calls are read from the data and may differ between conditions and in size.
• gt_preflight() audits a declaration: the batches, conditions and calls it implies or records, whether it describes the data, and, for inferred batches whose rows are stored call by call, whether that storage agrees with it. Implied calls count one for each batch, condition and declared replicate. A call with fewer items present than declared is reported, not treated as a mismatch. Recorded calls are counted from the rows supplied: the audit says that a call sent short and a call that lost rows afterwards look the same, that a call with no row left is not counted, and that positions are ranks among the items present, not necessarily the positions at which they were sent.
• A Gaussian fit models a declaration when the batches form a complete factorial with the facets: the same batches in every condition, each at the declared size. It estimates one shared call effect, reported as the source Call with its standard error, by the exact balanced likelihood, in which an item is a batch-by-slot cell. REML and ML fits match lme4’s in criterion and variances, including a call variance of zero. gt_preflight() says beforehand whether a fit will model the batches, and why not when it will not; its source table, parameter counts and strata describe the sources the fit will estimate, the call among them. Whether the batches were modelled is recorded for each fit, so a fitted design reused for another outcome family does not carry the earlier fit’s answer.
• A fit with a call effect has no reliability coefficients or decision study yet: the coefficient weights do not account for which items share a call, so gt_reliability() and gt_dstudy() refuse it. sequential, neighbor and by are recorded and not yet estimated.
• Every other declaration changes no estimate, and every result says what happened to a declaration: fits and diagnostics print the status, not_declared, declared_not_modelled with the reason, or modelled; coefficients and decision studies print it, their data-frame exports and gt_dstudy_target() carry batch_status, the plots state it where a caller’s own subtitle cannot replace it, and gt_report() records it. The exports gain that one column for every design.

Data checks, reporting and figures

• Outcome profiles describe category frequencies and representation across design groups, including absent declared categories and groups with no observed variation. These are descriptive summaries, not minimum-information rules or guarantees of accurate estimation. Example tables drop unsampled factor levels so bounded or empty tables do not retain hidden identifiers.
• Portable analysis reports collect model context, diagnostics, result tables and figures without refitting. Exports omit observations and group-level identifiers by default and distinguish retained fitting provenance from the environment used to generate the report. New discrete fits now record their fitting session before retention controls are applied; historical fits with no session record remain unknown.
• Preflight exposes bounded examples of missing cells and incorrect replication, without constructing the full Cartesian product or editing observations. Its panel-audit plot distinguishes complete, missing, under-replicated and over-replicated cells; a second view shows random-source dimensions. Counts are relative to observed coded levels.
• Reliability and D-study results have flat as.data.frame() methods. Allocation columns are prefixed, and D-study metadata is joined by design identity. Scale, extrapolation and uncertainty status travel with exported estimates, including unavailable intervals.
• gt_dstudy_target() screens one outcome or composite against an estimated G/Phi target within the supplied allocation grid. It preserves all candidates and ties; it does not search for a global or monetary optimum.
• Reliability forest plots show G/Phi and available estimation intervals. D-study plots add target references, series selection and extrapolation markers. These displays use base graphics and can be saved with ordinary PNG/PDF devices. The installed tutorial demonstrates the plots and exports.
• A separately frozen 40-fit binary/ordinal probit pilot retains every attempt: 38 accepted, one numerical rejection and one missing-category error. Rare-outcome point recovery can be poor despite acceptance. This is a small dense-engine study with explicit Monte Carlo uncertainty, not sparse qualification, a coverage study or a general operating-range claim.

Numerical safety

• The private sparse conditional solver enforces the same two validity invariants as the dense one (#46). The Newton step is checked against its own system by the shared backward-error rule, applied to the sparse Hessian without densifying it, and the final factor is probed with the same two fixed vectors before its log determinant enters the objective, through the factor object’s permutation-aware solve. A violated invariant refuses with sparse_newton_solve_invalid or sparse_final_factor_invalid, exactly as the dense solver refuses; there is no fallback, regularization or looser tolerance. tests/test_discrete_sparse_validity.R injects a corrupted step and a factor of a different matrix and checks the refusal, its reason and its retained measurements, on the sparse mode solver and end to end through the private evaluator seam. The frozen fixed-parameter, conditional-mode and fitted sparse contracts reproduce unchanged with the checks in place.
• A discrete fit’s starting-value refusal reports the evidence of the evaluation that refused (#43). The start is evaluated once, in detailed form, and there is no diagnostic replay: a second evaluation that happened to succeed would not be evidence that the first was valid, and one that failed would only describe itself. The established message and its reason=, backward_error=, solve_validity_bound=, random_dimension= and probe_index= fields are unchanged.
• Every objective evaluation the optimizer makes is performed in detailed form and reduced to its scalar by a bounded evaluation log, so an evaluation the optimizer could not use is described by its own record rather than by a recomputation. fit$diagnostics$evaluations records the count of evaluations, the count the optimizer could not use, the count of valid conditional solves that ended at the inner iteration budget, and, for at most eight invalid evaluations, the parameters asked about and the measurements reported; the conditional-mode diagnostic stage reports the three counts. The arithmetic is unchanged, because details selects what a mode solver returns and never what it computes.
• Setting the environment variable GTHEORY_DISCRETE_SPECIMEN_DIR to a directory writes a specimen file for each refused start and each retained invalid evaluation: the failed operation’s matrix, right-hand side, step and factor where the failure produced them, the parameters, the measurements and the identity of the numerical environment, as numeric objects that can be reloaded and replayed. It holds no observations. Off by default; a failed write is reported in the refusal message and never masks the refusal. SECURITY.md says so.
• A conditional solve that did not converge records how it stopped: inner_iterations, whether the line search failed to improve the penalised objective (inner_line_search_failed) and whether the final factorization was unavailable. Previously a failed evaluation carried no iteration count, so the retained scale-risk profile could not attribute its invalid evaluations; see the replay recorded in validation-studies/discrete-sparse-profile/results.md. The profile’s work pass now reports those stop causes.
• scripts/replay_specimen.R reads a specimen back and answers three questions separately: whether the stored step still fails the stored system, whether the stored factor still fails on this host, and whether a fresh factorization here fails too. Its verdict describes those observations without inferring a defective matrix, a responsible host, or portability from one replay. Reproducing the recorded backward error also requires agreement at numerical precision and the same solve-validity decision. tests/test_specimen_replay.R exercises it on dense and sparse specimens written through the real refusal site.
• The shared acceptance rule is mutation-checked in tests/test_interface.R: the FALSE-only rule it replaced accepts a discrete fixture whose flag is NA, and the test proves that its own assertion catches that.

Corrections

• A specimen of a tightened evaluation records the inner tolerance that evaluation ran with. The evaluation log passed the coarse control to the specimen writer for every phase, so a tight specimen reported 1e-7 for an evaluation that ran at 1e-9. The fit and its acceptance were unaffected.
• A solve- or factor-validity event is no longer lost behind routine non-converged evaluations. Retained records and specimens shared one allowance of eight, which an ordinary fit can use up on routine failures alone; a later validity event then left only a count. The evaluation log still retains the first eight invalid evaluations and now also retains later validity events until eight such events are held, so it holds at most sixteen records; fit$diagnostics$evaluations$validity_events counts every one. These limits and counts cover the optimizer’s logged evaluations, not final checks.
• A final check that fails with a structured refusal keeps that refusal’s reason, measurements and parameters in fit$diagnostics$final_checks, and writes its failed operation as a specimen when a directory is configured. Only a raised error’s text was kept before, so a refusal returned without an error blocked acceptance and left no account of why.
• A refused stationarity probe keeps the refusing evaluation’s record in the same way, in fit$diagnostics$outer_stationarity$failure, and writes its failed operation as a specimen when a directory is configured. Its retained error named only “invalid conditional mode”; for a solve- or factor-validity refusal it now also names the reason and its measurements. A probe that merely did not converge keeps the established message.
• The conditional-mode diagnostic stage reports the count of validity events beside its other evaluation counts.
• A preflight report no longer keeps the data in its recorded call. Called through do.call(), or with the data written inline, gt_preflight() stored the whole data frame in $call, unused columns included, whatever max_examples was. A data argument that is not a plain object name is now recorded as a marker, as a fit records it when its data is dropped.
• Fit summaries match variance-component standard errors and boundary flags by source and outcome separately. Names containing spaces can no longer make distinct source/outcome pairs select the same uncertainty row.
• The published 0.2.0 archive, manual, manifest and tag are retained unchanged.
• plot() on a decision study accepts col, pch, ylim, xlab and ylab through .... They were supplied explicitly before the dots were forwarded, so passing any of them failed with a duplicate-argument error; they are now defaults that a named argument replaces (#39, plotting).
• logLik(), and through it AIC() and BIC(), apply the same acceptance rule as gt_reliability() and gt_dstudy(), from one shared predicate: a Gaussian fit is refused on an explicit failure, and a discrete fit must carry an affirmative acceptance. A discrete object whose acceptance flag is missing or NA is refused rather than read as accepted. Freshly fitted objects are unaffected; every fitted discrete object carries the flag.
• A discrete fit produced through the private evaluator seam by a backend other than the dense one now reports that backend in its public engine (sparse_joint_discrete_laplace), derived from the retained marginal_backend, so a fit never describes itself as the dense engine when another implementation produced it. Dense fits are unchanged, and reliability, decision studies and preflight no longer key on the dense engine string. No public entry point selects the sparse backend.

Qualification study

• Calibration case K05 (cal_tail) was built with the five-level tail-mass construction on a three-level scale, because the level selector recognised the exact geometry name while the panel builder recognised any name containing “tail”: its categories were 15/60/165, with 68.75% of the panel in the top category. One explicit definition, EQ_TAIL_GEOMETRIES, now serves both, and the freeze test asserts the category composition of every tail-mass panel rather than only its digest. The original and corrected fixture identities are recorded as a pre-calibration amendment in CALIBRATION.md; the scored case C12 and every other fixture are unchanged, and no tolerance had been derived from the defective panel.
• The retained 2,400-row scale-risk specimen was replayed with the failed evaluations’ own records: every invalid evaluation was conditional-mode non-convergence, none was a solve or factor validity event, and the backend disagreements were gradients straddling the relaxed final tolerance at the iteration budget in the tightened phase. Recorded in validation-studies/discrete-sparse-profile/results.md.
• The measurement warm starts are conditional on (#5) was taken on that specimen with budget-experiment.R: a doubled inner budget recovers 32 of the 48 unusable evaluations and all 40 marginal solves cold, and a warm start from the previous evaluation’s mode recovers 38 of 48 at the ordinary budget in a median of 9 iterations; 16 evaluations do not converge cold within 300 iterations and are not a budget question. The roadmap and development status now state the 0.4.0 release gates and this line’s position against them.

Gtheory4LLM 0.2.0

Cut from the 0.2.0 development line for the CRAN resubmission. Every estimate, coefficient and acceptance decision in tests/package-characterization.R reproduces the 0.1.0 values within the tolerances recorded there. The one change to fitting itself is the refusal of a dense conditional solve that does not solve its own system, recorded under engineering below. Everything else is new reporting, corrections, engineering behind private seams, and the release maintenance that followed 0.1.0.

Staged numerical diagnostics

• gt_diagnostics() gains a stages element summarising the numerical checks a fit passed through: optimizer completion, conditional mode, independent stationarity, restart and tolerance stability, numerical acceptance, and approximation assessment, each with a status, a reason and its supporting measurements, so a rejection names the stage responsible without the caller reading private optimizer records. The status is passed, failed, not_assessed for a check that did not run or does not apply to the engine, or inconclusive for one that ran without a clear verdict; absent evidence is never read as a pass. The summary is derived from evidence the fit already retained, including the tolerances that governed the retained solve: it never refits and never changes an acceptance decision. Print methods are registered for the stage summary and for a single stage.
• The conditional-mode diagnostic stage no longer reports passed when a record says its solve was tightened but does not retain the tolerance that governed it. That record now reports inconclusive, because the retained evidence cannot establish that the solve met what was asked of it. This changes reporting only, and only for incomplete or older fit objects: a fit produced by this version retains that tolerance. Fitting, numerical acceptance, and reliability and D-study eligibility are unchanged.

Corrections

• README links to repository documents that the source archive does not ship (docs/, scripts/VALIDATION.md, validation-studies/, artifacts/, SECURITY.md, LICENSE) are now absolute repository URLs. CRAN’s incoming check reported the relative scripts/VALIDATION.md and LICENSE links of the 0.0.6 submission as invalid file URIs; a test now refuses any README or NEWS link to a file that .Rbuildignore keeps out of the archive.
• SECURITY.md named a control that does not exist. The retention switch is gt_control(retain = list(data = FALSE)).
• BIC() on a REML fit is documented, and its convention, N response vectors with the covariance parameters only, is recorded as reml_BIC_response_vectors_variance_parameters. help("gt_fit") no longer says the generic criteria are unavailable for REML: the fit’s own AIC and BIC elements are NA, the generics are not.
• load_functions.R sources R/discrete_sparse_mode.R, matching the Collate field; a test keeps the two lists equal.
• print(gt_diagnostics(fit)) states the standard-error availability once.
• A Gaussian source flagged at the boundary is now classified as entirely zero or as singular but nonzero, and interior-block standard errors say which they condition on. A rank-deficient source with positive variances was described as “held at zero” in the fit’s interpretation, its diagnostic issues and the printed coefficient note; it is now described as held fixed at its fitted covariance, from one shared clause. gt_component_vcov() adds conditional_on_fixed naming every fixed component, and conditional_on_zero now names only the zero ones. The boundary flag, the point estimates and the interval arithmetic are unchanged (#36).
• logLik(), and through it AIC() and BIC(), refuse a fit that failed numerical acceptance, as gt_reliability() already did; the objective stays in minus2loglik for diagnosis. The generics previously returned ordinary values while print(fit) called the estimates diagnostic only.
• gt_control(retain = list(data = FALSE)) now removes every copy of the observations. The retained Gaussian OpenMx model carried the raw outcomes as summary metadata, so a fit saved after dropping the data still held them, and the recorded call held the whole data frame, unused columns included, when the fit came through do.call() or with the data written inline. The observations are stripped from the retained model, which nothing reads after fitting, the call’s data argument is replaced by a marker, and a test hunts a sentinel observation and a sentinel unused column through the serialized fit for direct, programmatic and inline calls. SECURITY.md says so.
• Gaussian facet levels are matched by value rather than by their printed form. Distinct doubles that format alike, such as 1e15 and 1e15 + 1, passed gt_preflight() and were then refused by gt_fit() as duplicate cells; they now fit (#39, facet identity).
• Gaussian outcomes are centred before the factorial contrasts are formed. The intercepts are profiled exactly in a balanced design, so no likelihood term changes, but a large common offset no longer enters the contrasts as differences of huge sums: an offset of 1e12 moved accepted estimates in their fifth digit and 1e15 in their second. A regression fits the same represented panel at the origin and at an offset of 1e12 and requires identical variances and deviance.
• summary() of an ordinal fit no longer lists the outcome’s location under “Fixed location or contrast estimates”: it is fixed at zero for identification and is now printed as such. Binary intercepts and categorical contrasts are still printed as estimates.
• A discrete fit’s design notes, and so gt_diagnostics(), no longer carry the constructor’s specification-only note after the engine’s data checks have run; the discrete path now records its validation scope through the same step the Gaussian path uses.
• The limitations page states the balance requirement per engine: the Gaussian likelihood needs a complete balanced panel, the discrete Laplace likelihood accepts missing whole cells at the declared replication, and reliability and D studies need the complete panel in either case.
• scripts/prepare_release.py --from-checked-candidate refuses a candidate directory without a check report recording a successful R-devel check of exactly those archive bytes, and records the checking R version in the manifest’s provenance.

Engineering behind private seams

• The dense conditional solve now refuses a factorization that does not solve its own system (dense_newton_solve_invalid, dense_final_factor_invalid), reporting the backward error and its bound when the refusal happens at the starting values. A native Cholesky can return successfully and still hand back a factor of some other matrix; issue #14 captured one such instance on a hosted runner, and a returned factor is no longer treated as valid merely because no error was raised.
• The discrete engine is split into response-kernel, dense, mode and sparse modules without numerical change. Matrix and methods joined Imports for the private sparse random-design, Hessian, factorization and conditional-mode files behind a private evaluator seam. None of it is public API: no public entry point selects the sparse path, and every fit still runs through the dense engine. The compatibility matrix runs the sparse factorization test on every platform.

Release maintenance after 0.1.0

• Keeps archive source-version summaries publication-neutral; publication updates only excluded repository metadata. Preparation rejects an already tagged or published version before building.
• Separates the development checkout from the published 0.1.0 archive and manual, whose files and tag remain unchanged.
• Updates installation instructions to the published GitHub release, records the configured branch protection and publication checks, and moves completed 0.1.0 hardening out of the future roadmap.
• Distinguishes the rebuilt R-devel candidate from the published archive; candidate-check evidence applies to the file actually checked.
• Catches stale publication claims outside the release summary blocks and records the active rule preventing release-tag updates and deletions.

Gtheory4LLM 0.1.0

First 0.1 release. An earlier 0.1.0 candidate was prepared locally but never tagged or published, so this release is cut from the hardened sources instead and supersedes it; the notes below cover both. The statistical models are unchanged from that candidate: every estimate, coefficient and acceptance decision in tests/package-characterization.R reproduces its values within the tolerances recorded there.

Numerical baseline

• Adds a characterization baseline over ten canonical cases (Gaussian ML and REML, multivariate, a variance boundary, fixed facets, a nested design, binary, ordinal and multinomial Laplace, and a deliberately rejected discrete fit). Optimizer-dependent quantities are compared with tolerances, acceptance decisions exactly. Regenerating it is deliberate and must be explained here.

Nested designs

• Adds reliability, mixed fixed/nested weighting, and decision-study regression tests for p x (i:h) and for p x (i:h) x r with a fixed facet, with every expected coefficient written out from the published formulas rather than produced by the code under test. Nested designs were previously exercised only at the specification level: no nested model was fitted, and no nested reliability or decision study was computed.
• help("gt_design"), help("gt_reliability") and help("gt_dstudy") now define balanced coded panel explicitly, with a worked contrast between globally unique child labels and within-parent codes.

Optimizer retrying

• Replaces transcript-parsing retry accounting with a retry controller that drives the optimizer directly, in R/gaussian_retry.R. Every attempt now records a real optimizer status; previously a trial run inside OpenMx’s own retry loop could be recorded with an unknown status, and a change in OpenMx’s message wording could fail an otherwise valid fit.
• fit$retry_attempts gains start_type (replacing start) and optimizer_success, and drops the transcript-specific native_attempt, invocation, continuation_reason and native_returned_fit columns. attempt, optimizer, status, minus2loglik, external_accepted, external_rejection_reason, error and returned_fit are unchanged. fit$retry_settings$native_invocations becomes optimizer_runs.
• The retry policy itself is unchanged: at most extra_tries + 1 runs, each unsuccessful run followed by OpenMx’s own bounded uniform perturbation of the best model so far, stopping at the first externally accepted run.

Standard methods

• Adds logLik(), nobs() and coef() methods for gt_fit, and a print method for gt_diagnostics(), which now returns a classed object.
• AIC() and BIC() reproduce the fit’s own recorded conventions: an ML fit’s ml_AIC and ml_BIC_response_vectors, a REML fit’s reml_AIC_variance_parameters. A REML logLik carries REML = TRUE; a discrete one is labelled as a first-order Laplace approximation.
• Adds gt_component_vcov() for the sampling covariance of the estimated source covariances, which is what gt_reliability() propagates into an interval.
• vcov() on a fit raises an error rather than returning that matrix. In R, vcov(fit) is the covariance of coef(fit), and generic tooling relies on the pairing; this package does not estimate it, because Gaussian outcome means are profiled out of the likelihood and the discrete engine computes no observed information. The error names the reason and points at gt_component_vcov().

Controls

• gt_control() gains retain, choosing which optional components a fit keeps: data, model, retry_log and session. Every default is TRUE, so an existing call is unaffected. Dropping all four reduced a 600-row Gaussian fit from 525 KB to 64 KB with every reported result identical, including reliability, decision studies, diagnostics, correlations and the component covariance.
• Every fit now records a compact panel summary, so reliability and decision studies remain available when the modelled data was not kept. Where the data is kept it is still what the balanced-panel rules are checked against, so a panel edited after fitting is still caught.
• The discrete engine gains max_dense_bytes (default 512 MiB), checked before allocation. The other discrete limits bound counts; this bounds the dense algebra those counts imply, which is what protects a caller who raises them. The refusal names the estimate, the limit, the observation count, the random dimension, the size of the random-design matrix, and the alternatives. It does not bind for any model the existing count limits already allow.

Release and repository

• artifacts/manifest.json declares release_state, and the marked README and NEWS blocks declare it too. Publication is now checked against git: a published claim requires the version tag, and a prepared claim fails once that tag exists. Nothing calls a bundle published before it is.
• Adds scripts/prepare_release.py, which derives every version from DESCRIPTION, validates, builds the archive and manual, writes the manifest, rewrites the release blocks, verifies the result, and stops. It never tags, pushes, uploads or submits.
• scripts/check_public_contents.py no longer hard-codes the version: the package name comes from DESCRIPTION and the bundle version from the manifest, which is what lets a development checkout retain the preceding bundle.
• Adds docs/REPOSITORY_POLICY.md with the required branch-protection state, docs/branch-protection.json as the exact payload, and scripts/check_branch_protection.py to verify it. The declared branch protection was applied to main after the release was published.
• Adds SECURITY.md and CODEOWNERS.
• The reference manual build falls back to plain R CMD Rd2pdf when R’s generated LaTeX is not the layout its customized cover knows how to reflow, and reports which route it took and whether the overfull-box gate could run. The manual gate now requires both pdflatex and makeindex before claiming it can run, and names the missing tool when it skips.
• The renv bootstrap is described by scripts/dependency-locks/renv-bootstrap.json, verified by SHA-256 before installation, and preferentially taken from a cache that both locked workflows now keep. Pinning a version says which renv is used; the digest and the cache are what make retrieving it checkable and possible.

Fixes

• plot() on a decision study now reports an invalid coefficient instead of failing on a zero-length condition.
• gt_preflight() and gt_fit() now share one definition of the covariance and residual rules, so a request one accepts is a request the other accepts. Previously preflight admitted a Residual covariance override that fitting refused, and fitting completed abbreviated residual names that preflight rejected. Both now refuse both, with the same message.
• A local variable named T in the discrete engine no longer shadows the TRUE alias.
• Four overfull boxes introduced by the new help pages are fixed in the Rd content rather than by relaxing the gate that found them, and the locked workflow now preserves the manual’s LaTeX log and rendered PDF as build evidence: the log is the only place that says which box overflowed.
• The renv bootstrap digest is recorded and enforced. It was computed from an independent download of the pinned URL and matches the digest the locked workflow computed; a mismatch now refuses to install.
• During release preparation, the install instructions pointed to the local prepared bundle. The development notes above record their subsequent update to the published GitHub release.
• docs/LIMITATIONS.md collects every limitation in one place; the other documents link to it. docs/ROADMAP.md records planned work. docs/DEVELOPMENT_STATUS.md now holds only the current state.

Release scope

The capability this release ships, unchanged from the earlier candidate.

• Consolidates the reviewed development API for univariate and multivariate Gaussian models and small binary, ordinal, and unordered categorical models. Designs support explicit crossed/nested random sources, selected item interactions, and source-specific covariance. The numerical engines and fitting limits are unchanged from 0.0.7.
• Retains exact balanced Gaussian ML/REML, Gaussian variance standard errors and coefficient intervals, supported fixed-facet reliability, and balanced decision studies. Discrete likelihood uses first-order Laplace integration with Gaussian random effects; binary/ordinal coefficients require the latent scale, and discrete uncertainty intervals are not implemented.
• Includes the three public LLM annotation panels and an executable real-data and failure tutorial explaining native outcomes, model choices, numerical diagnostics, and full-panel discrete fitting limits.

Reproducible statistical pilots

• Adds protocols, seeds, source fingerprints, environment records, all attempted replicates, and summaries outside the installable package.
• Records 320 Gaussian fits across four univariate settings and ML/REML, with 40 replicates per setting/estimator. Observed coefficient coverage was 90–100%, with wide Monte Carlo intervals; these pilots do not establish a general coverage guarantee or validate boundary-conditioned inference.
• Records 180 fixed-parameter discrete panels across binary and ordinal logit/probit and nominal softmax models. Independently integrated references converged under refinement; 165 package comparisons were available and 15 were unavailable because a declared category was absent. Nonzero Laplace errors are retained rather than treated as software-test failures.
• Records 40 binary recovery fits across four settings. All were numerically accepted, but short panels produced variable variance estimates and boundary solutions. Numerical acceptance does not establish accurate recovery.
• These results were generated against the recorded 0.0.7 source. Their original provenance remains unchanged; the 0.1.0 release does not imply a new simulation campaign or expanded statistical operating range.

Release and validation infrastructure

• Corrects release-version prose and checks DESCRIPTION, the archive manifest, release summaries, and optionally the release tag for consistency.
• Covers all pull-request and main-branch changes in CRAN candidate readiness, pins external Actions to verified commits, and cancels superseded runs.
• Makes compatibility checks retrieve the recorded release source history needed to verify committed artifacts.
• Adds a documentation dependency lock and drift checks while preserving the numerical dependency versions. The locked workflow restores both stacks; current-R candidate checks retain their separate environment.
• Clarifies parameter uncertainty versus new-panel prediction, fixed-facet choices, and what temperature-specific agreement can establish.
• Adds contribution, release, and statistical-validation scope guidance.

The version change does not add sparse fitting, unbalanced Gaussian estimation or reliability, cost-aware planning, bootstrap/jackknife, or discrete intervals. GitHub publication and package checks are separate from CRAN submission and acceptance.

Gtheory4LLM 0.0.7

Fixes found in review of this release

• The variance covariance coordinates, which auto now selects for every univariate or diagonal discrete model, treated any negative coordinate as an error. A bounded optimizer evaluates a few ulps outside a bound while projecting onto it, so a coordinate of -3.4e-17 recorded an attempt error, set computation_failed, and rejected the entire fit. Zero is this parameterization’s natural domain boundary, so rounding noise is now projected onto it; a coordinate meaningfully below zero still stops. This rejected fits whose estimates were already correct, including the ordinal fit in examples/standalone_usage.R.
• The same rounding noise could also appear in the parameter vector a bounded optimizer returns, not only in the points it evaluates. A converged result reported a few ulps outside its bound was discarded as “no usable finite result”, which failed the fit. Returned parameters are now projected onto their bounds with a tolerance that scales with each bound’s own magnitude, so a point meaningfully outside is still refused.
• A source variance resting on the boundary now reports NA rather than a number whenever the joint Hessian happened to stay invertible, matching what it already reported when the Hessian did not. No symmetric interval follows from curvature at a boundary. The full entry covariance matrix is unchanged in $uncertainty, and coefficient intervals still use it.
• The interval caveat names at most three sources inline and otherwise gives a count and a pointer; on the bundled panels it listed all thirteen.
• Adding a vignette made R CMD build write build/vignette.rds and inst/doc/ products into the archive, which no release gate expected. The public-content audit rejected the build index as an unexpected build file and then as a malformed example resource; the committed-artifact gate would have reported every generated vignette product as content missing from git at the next release. Both now account for them, and inst/doc/ products are derived from the vignettes declared in the source commit rather than excused by prefix, so an unexpected file there still fails.
• When knitr, rmarkdown, or pandoc is absent, R CMD check failed the whole package on Packages suggested but not available, reporting a missing documentation toolchain as a package defect. The gate now checks with _R_CHECK_FORCE_SUGGESTS_=false in exactly that case, and accepts the single extra --as-cran line about a missing vignette index only when vignettes were genuinely skipped, so it can never excuse a real one.
• The locked Linux environment could not build rmarkdown because fs needs libuv headers, and install.packages() only warns when a build fails, so the step passed while the toolchain was absent. The workflow installs libuv1-dev and the step now asserts the result instead of reporting success either way.
• Clarifies that fixed = "temp" selects a reliability estimand after fitting; it does not alter the fitted variance model. A within-temperature analysis is one conditional sensitivity analysis when pooling is questionable.

Uncertainty for Gaussian fits

• Reports asymptotic Wald standard errors for every Gaussian source variance. The engine already computed the restricted- or profile-likelihood Hessian for its diagnostics and discarded the result; it now forms the parameter covariance matrix 2 * H^-1 and keeps it. Reconstructed values match OpenMx’s own standard errors to machine precision and match the classical mean-square formulas for a crossed two-way design to five significant figures.
• Adds delta-method standard errors and logit-scale intervals for G and Phi in gt_reliability() and gt_dstudy(), per outcome and for weighted composites. New level argument; new Erho2_se, Erho2_lower, Erho2_upper, Phi_se, Phi_lower, and Phi_upper columns. plot.gt_dstudy() draws the bounds. Deterministic tests check the delta-method mapping. A previously reported pilot coverage range has no reproducible protocol/results in the public repository and does not define a validated operating range.
• A zero-boundary variance can accompany unusable joint Hessian curvature, which previously removed every standard error. Standard errors are now computed on the interior block, conditional on the zero components being held at zero, and both the fit and the printed coefficients say so. A component held that way reports NA, never a structural zero that would read as certainty.
• Standard errors are unavailable, with a stated reason, when check_hessian is disabled or the curvature is unusable. The discrete Laplace engine computes no observed information and says so rather than leaving the field empty.

Mixed-model fixed facets

• Adds fixed to gt_reliability() and gt_dstudy(), implementing the mixed model of Brennan (2001): the object-by-fixed-facet variance is averaged over that facet’s levels and joins universe-score variance, a source built only from fixed facets shifts every object equally and leaves the model, and every other source keeps its usual divisor. With no fixed facet the decomposition is unchanged. $source_roles records the role each source took.
• A fixed facet’s count cannot be changed and a decision study cannot project over it, because its universe is exactly its observed levels. Declaring every facet fixed is refused: that design has no estimable error variance.

Designs and documentation

• Adds full_cell to gt_design(). full_cell = FALSE removes exactly the object-by-all-facets source and leaves every other requested source unchanged, which is how a binary or ordinal study with one observation per cell declares the default crossed design. The discrete rejection message now names that argument. The source is still never dropped automatically.
• Converts the installed tutorial into a real knitr vignette, so browseVignettes("Gtheory4LLM") finds it. system.file("doc", "LLM-workflow.R") and the installed HTML keep working.
• Makes man/*.Rd and NAMESPACE the only documentation source of truth. The R files previously carried roxygen blocks that were not the source of the richer hand-written Rd pages; running roxygen2 would have replaced them and dropped the S3 methods registered in NAMESPACE. Those blocks are now plain comments.
• Documents temperature and seed as study-design choices. Observed agreement differences across temperatures motivate diagnostics but do not by themselves prove heterogeneity of latent random-effect variances.
• States that the native discrete codings of the bundled panels exceed the dense discrete engine by more than an order of magnitude, and explains why the declared R (>= 4.5.0) floor comes from OpenMx’s under-declared C API requirement rather than from this package’s own code.
• Repairs two tests left inconsistent by the covariance_parameterization default change, and adds installed-package regression tests for standard errors, the delta-method mapping, coefficient intervals, mixed-model fixed facets, and full_cell.
• Stops the public-content audit from failing a working-tree scan on operating system metadata such as .DS_Store, which git already ignores and a desktop environment recreates. The exemption is by exact filename, is reported under skipped_os_metadata, and never applies inside an archive.
• The package build now knits the vignette, so scripts/check_package.py needs knitr, rmarkdown, and pandoc. When they are absent it records that, builds with --no-build-vignettes, checks with --ignore-vignettes, and the installed-tutorial stage prints NOT RUN rather than passing silently. The three workflows install the toolchain.

Smaller interface and reporting changes

• Adds concise reliability and decision-study print methods while preserving complete results and diagnostics; resets coefficient row names.
• Adds counts to gt_reliability() with the existing design argument retained as a compatibility alias.
• Labels and retains Gaussian derivative-pass warnings separately from optimizer acceptance, and removes stale pre-fit validation notes after preparation.
• Improves unsupported full-cell and balanced/nested design guidance without changing the requested statistical model.
• Defaults discrete covariance coordinates to auto: direct variances for univariate/diagonal models and log-Cholesky for joint unstructured models. Explicit choices and numerical acceptance criteria remain available.
• Corrects the public package citation and explains full-panel discrete limits, mental-health preprocessing, and coefficient interpretation more directly.
• The 0.0.7 source archive and reference manual remain available in the v0.0.7 GitHub release.

Gtheory4LLM 0.0.6

• Prepares the package for its first public distribution under GPL-3.
• Bundles eight outcome sets and fifteen codings of three real, publicly archived LLM annotation panels. Preserves public-source checksums, CC BY 4.0 data attribution, response mappings, and the three dataset reference chapters. Rebuilding modeling resources accepts explicitly supplied public-source CSVs; verification requires no external files or network access.
• Adds gt_preflight() to report resolved sources, covariance and random-effect dimensions, replication/completeness, resource limits, and supported scales.
• Adds an installed synthetic LLM tutorial completing fitting, diagnostics, G/Phi calculation, and an 18-allocation decision study.
• Makes installed example loading independent of ambient workspace variables; intentional directory overrides support RDS resources and source CSV files.
• Adds a concise classed fit summary and shared diagnostic fields for selected attempts, rejection reasons, natural covariance boundaries, and artificial parameter bounds. Existing estimates and numerical acceptance rules are unchanged.
• Preserves explicit starting values, fixed optimizer selection across fitting attempts, reproducible retry controls, and failed-fit safeguards.
• Requires R 4.5 or later because the supported OpenMx dependency uses an R 4.5 API.
• Adds minimum-R and Windows/macOS compatibility workflows beside the locked Linux numerical checks, plus one source-and-artifact release-check entrypoint.

Scope at 0.0.6 was exact balanced Gaussian likelihood and small-model first-order Laplace discrete likelihood. Discrete latent random effects remain Gaussian. Binary/ordinal reliability requires an explicit latent scale; unordered categorical scalar reliability, observed-score discrete reliability, joint Gaussian-discrete fitting, bootstrap/jackknife, and uncertainty intervals of any kind were not implemented in that release. (The development version adds asymptotic Wald standard errors and delta-method coefficient intervals for Gaussian fits only; see above.) Numerical tests do not establish parameter recovery, approximation adequacy, or application-wide statistical validity.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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