Changelog

Source: NEWS.md

tulpaRatio 1.4.3

• temporal_tvc(structure = ...): "iid" unblocked, "gp" now errors instead of silently downgrading to RW1, and the Gibbs backend now rejects anything but "rw1" instead of silently ignoring the argument (#32). TemporalType::IID had a complete log-prior and gradient in both the H and autodiff paths (tvc_term_log_prior, tvc_prior_gradients_ws, and the templated density via hmc_tvc_autodiff.h’s using declarations), and the C++ string parser already recognized "iid" – but temporal_tvc()’s match.arg only offered "rw1"/"rw2"/"ar1"/"gp", so no R call could ever reach it. structure = "iid" is now accepted, verified against test_gradient_check.cpp’s finite-difference harness in both H and A_r modes (0 deviation), and recovers a simulated intercept and slope in test-recovery-constrained-fields.R. Separately, "gp" was accepted and documented as a first-class option, but TVCData has no GP fields at all (no lengthscale, no covariance branch) – the C++ parser silently mapped any unrecognized string, "gp" included, to RW1, so temporal_tvc(..., structure = "gp") ran to completion and silently fit an RW1 varying coefficient. temporal_tvc() now errors immediately if structure = "gp" is requested, rather than reaching the sampler at all. Independently, the Gibbs backend’s TVC update (# ---- 5. Update TVC coefficients via univariate MH with RW1 conditional proposal ----) is hardcoded to RW1 and never read the tvc_structure field R was already sending it, so mode = "gibbs" silently ran RW1 regardless of what structure asked for; the Gibbs backend now rejects any TVC structure but "rw1" with a clear error pointing at mode = "hmc". The C++ side’s own duplicated string-to-enum parser in hmc_sampler.cpp was replaced with a call to the existing ratiod_tvc::parse_tvc_structure(), which had no callers before this.

• spatial_car(..., proper = TRUE) now fits a proper CAR instead of silently downgrading to ICAR (#31). The R object already computed rho bounds and printed “Proper CAR”; a complete C++ implementation (hmc_car_proper.h) already existed too. Neither was wired to the other – every backend collapsed type = "car_proper" to "icar" before it reached C++ (# Handle all ICAR-type models (car, car_proper both use ICAR parameterization)), so rho stayed fixed at 1 and the roxygen example’s claim that summary(fit_car) shows a rho_spatial parameter was false. The HMC backend now threads rho through as its own parameter (Q(rho) = D - rho*W, Beta(1,1) prior by default, logit-transformed), added to SpatialType, ParamLayout, and the shared spatial_gmrf_prior_grad/ compute_log_post machinery that compute_gradient_composite and every H gradient mode already read from. phi’s gradient is exact; rho’s log-determinant term has no cheap closed form (it needs trace(Q^-1 W)), so it is central-differenced on that one scalar and chain-ruled to logit scale, verified against test_gradient_check.cpp’s finite-difference harness (0 deviation across all 29 parameters on the car_proper field). Proper CAR is full rank for rho < 1, so unlike ICAR/BYM2 it is not hard-centered. Autodiff gradient modes (A, A_r, A_t) refuse at the front door with a clear error rather than differentiate a density missing the field’s prior entirely, since the log-determinant is not yet expressed in the templated density; mode = "hmc" with the default gradient_mode = "auto" (or "H") is unaffected. Gibbs already rejected car_proper (its type whitelist never included it); Laplace, PG, VI, SGHMC, and ESS now reject it explicitly too, instead of silently fitting an ICAR model or – for the three backends that call compute_log_post_impl<double> directly – silently dropping the spatial prior’s contribution to the posterior altogether. Fixing this also surfaced a second, independent bug in predict()’s areal-prediction path, which read phi_spatial starting one column early for any type it treated as “ICAR-shaped” once car_proper genuinely had an extra rho parameter in front of it.

• A spatiotemporal Type IV interaction has a non-centered parameterization, which fixes chains freezing on data with little or no true interaction (#24). spatiotemporal(..., type = "IV") reached rhat 1.14 at iter=1000/warmup=500 on data simulated with no space-time signal, and a 4x longer run moved that to 2.05 while every chain’s own posterior sd shrank. The interaction’s precision tau has no likelihood signal pulling it away from the boundary in that case, so its posterior drifts to very large values (tau ~ 4.3e8 was observed at warmup’s end); under the centered parameterization the field’s conditional scale shrinks as 1/sqrt(tau), so exploring both regions needs step sizes that differ by orders of magnitude, and NUTS answered by pinning the trajectory length at max_treedepth on essentially every iteration and collapsing the step size toward its floor – the signature of a funnel between a variance component and the field it scales, not of a chain that merely needs more draws. A finite-difference check of compute_gradient_spatiotemporal_handcoded’s Type IV (Kronecker) block, absent until now, confirmed the analytic gradient was not the cause. spatiotemporal(..., parameterization = "noncentered") samples a tau-free z and reconstructs the interaction as z / sqrt(tau), decoupling the two: rhat on the same repro drops to 1.00 at iter=1000 and stays at 1.00 at iter=4000, with stable per-chain sd and a step size about 10x larger. The non-centered path already existed in the gradient code but was never reachable from R and was itself unchecked against finite differences; the warmup-end precision-informed mass-matrix warm-start specific to Type IV (which reads the raw parameter as the centered field directly) is now skipped under the non-centered parameterization, which it does not describe. parameterization still defaults to "centered", since data that do carry real space-time structure move tau away from the boundary and may not need it.

• The Gibbs BYM2 sampler no longer leaks the spatial field’s level into the intercept unchecked (#15). The per-site update held the structured field (phi) to sum zero by moving its mean into the intercept every sweep, unconditionally: no acceptance test weighed that move against the intercept’s own prior. dev_notes/gibbs_prior_recovery.R (now tests/testthat/test-gibbs-prior-recovery.R) fits with zero binomial trials, where the likelihood is flat and the posterior must equal the prior exactly; under a N(0, 0.5^2) intercept prior the sampled sd came out at 1049 instead of 0.5, a ~2000x inflation. The move now competes for acceptance like every other update that touches the intercept, and gained a dedicated block-level move (mirroring the one the unstructured component already had) so the now-resisted level direction still mixes: BYM2 intercept ESS is 749 at iter=2000/warmup=1000 where it used to need the same bar ICAR clears at 1000/500. The Gibbs priors already matched compute_log_post’s (Half-Cauchy sigma_total, Uniform(0,1) rho, Gamma tau, threaded through fit_gibbs() via priors) from prior work on this issue; this closes out the remaining checklist items, including removing the unused pc_lambda left over from an earlier tau update.

• A per-fit cores budget can now reach within-chain work (#17). For a single-chain HMC fit, cores used to be capped at chains before the user ever saw it (min(chains, cpp_get_max_threads())), so a one-chain fit always got exactly one thread for its gradient and likelihood loops regardless of machine size, and the regions that do carry a num_threads() clause (compute_gradient_analytical’s fallback path, the linalg_fast.h kernels) had no way to reach more than one. cores is now a genuine budget split two ways: concurrent_chains = min(cores, chains) chains run at once, each with cores %/% concurrent_chains threads of its own. Reaching that width when chains also run concurrently needed OpenMP nesting enabled (omp_set_max_active_levels(2)): a region inside a chain body is a nested one, and OpenMP pins nested teams to one thread by default. Most common model configurations (plain Poisson-gamma/binomial/negbin with simple random effects) take a vectorized Eigen gradient path that never reaches these OMP regions at all; the scalar fallback that does carry them serves zero- inflation, correlated random slopes, and GP/multiscale spatial models.

• The two log posteriors are now the same function (#18). compute_log_post and the templated compute_log_post_impl<double> that the A_r / A / A_t gradient modes differentiate returned different numbers on every model and omitted different structures. For those modes the runtime gradient check differences that same templated density, so a structure missing from it was invisible to both the gradient and its own numerical reference: they agreed on the same wrong value. Differencing the two densities against each other instead, over every structure the parameter layout allocates for, is what found the following.

  structure	before	after
  HSGP spatial field	absent: all 11 parameters gradient exactly 0, worst relative deviation 1.00	1.3e-7
  random slopes, uncorrelated	slope variance absent, 1.00	1.2e-9
  random slopes, correlated	Cholesky factor and variance absent, 1.08	1.0e-8
  crossed random intercepts	second term absent: 5 effects and its variance 0, 1.33	1.2e-9
  TVC precision	a different prior on each side, 0.376	4.2e-10
  collapsed ICAR / BYM2	read params[-1], access violation	refused

  The residual constant that remained on every model was the binomial coefficient: the eta-form likelihood drops it as constant in eta, which is right for a Laplace inner solve and wrong for a log posterior that is reported and compared. lp - lp_impl was a flat -7156.27 across all fields and is now 0 to floating-point noise.

• A collapsed spatial field refuses an autodiff gradient mode instead of crashing. The collapsed parameterizations marginalize the field out by locating its mode with Newton and adding a Laplace correction, which is not a closed-form function of the parameters, so the templated density cannot express them and the layout allocates no slot for the field. Reading that absent slot was an access violation. gradient_mode in "A_r", "A", "A_t" now errors at the front door naming the parameterization; "H" and "N" carry the analytic density and are unaffected. The same declaration covers the HSGP multi-scale GP and the non-centred GP, which are expressible but not written yet (#26).

• The TVC precision takes the prior it is configured with. tvc_tau_shape and tvc_tau_rate reach the sampler from priors and defaulted to Gamma(2, 0.5), but two of the three implementations ignored them for a hard-coded PC prior with P(sigma > 1) = 0.01. The density, the specialized gradient and the composite gradient now all read the configured hyperparameters, through one derivative helper.

• The random-effect prior is one implementation. hmc_re.h computes the prior and the effective effects for every layout the sampler allocates – one or more crossed terms, intercept-only or with slopes, correlated or not, centred or non-centred – and both densities call it, so a term cannot be added to one and not the other. The TVC quadratic forms, per-term prior and eta assembly move the same way: templated in hmc_tvc.h and re-exported to ratiod_tvc_ad, replacing copies that had already drifted on the cyclic wrap-around edge. The RW1/RW2/AR1 templates take a pointer, so a caller holding a strided slice passes an offset rather than copying into a vector.

• cpp_gradient_check() reaches the structures it could not build before (hsgp, tvc, re, re_crossed, re_slopes, re_slopes_corr, icar_collapsed, bym2_collapsed), differences either density via reference =, and reports log_post_impl and impl_gap alongside the gradient. test-gradient-correctness.R asserts across every field that the two densities return the same number, that no block has an identically-zero gradient under any autodiff mode, and that a collapsed model refuses one. Its tolerance now allows an absolute 1e-6 before the relative comparison: a central difference of a log posterior of magnitude 1e3-1e4 resolves a gradient entry to about 1e-7, and the HSGP basis coefficients run to 6e-4, where 1e-4 relative asked for more than the difference can deliver.

tulpaRatio 1.4.2

• Every intrinsic field is now identified at the engine’s constant (#12). s2z_precision(n) returns 1 / (kappa * n)^2 from sd(sum phi) = kappa * n, so callers pass a PRECISION. The spatiotemporal and TVC penalties took that constant as an argument and their call sites filled it with a bare 0.001 – which is kappa itself, weaker by ~2.5e6 at T = 20, pinning the field’s sum at sd 31.6 instead of 0.02. Each penalty now derives the constant from the length of the sum it pins, so no call site can supply one. The interaction margins take their own constants rather than sharing one: a space margin sums S terms and a time margin T.

• SVC keeps its own constant, deliberately. Its penalty is rewritten on the sum rather than the mean – the two forms are the same penalty, related by a factor of n_obs – and both it and its gradient now read one svc_sum_to_zero_ridge(). That constant is NOT s2z_precision(n_obs): an NNGP field’s prior is proper, so its mean is identified and what is wanted is a ridge, not the pin a rank-deficient field needs. Substituting the intrinsic constant was tried and reverted – at N = 80 it is 156.25 against the ridge’s 0.0125, and at that stiffness 4 chains x 1000 iterations return in 9 seconds with per-chain posterior SD 0, Rhat 15.1 and the slope at 0.095 against a truth of 0.3, where the ridge samples for minutes. Deriving the right constant for a proper field on its own terms is #25.

• The multiscale trend and seasonal arms are pinned (#12). Neither carried a sum-to-zero term on any path. Both are intrinsic and both enter the same linear predictor, so each had a constant null direction aliased with the intercept and with the other – a two-dimensional ridge the fitted series hides, because only the level moves. Both are now pinned in the log posterior and in the analytic gradient; the proper short-term arm (AR1/IID) identifies its own level and is left alone. On a 500-observation binomial fit against a known intercept of 0.5, over 4 chains:

  trend + seasonal	intercept	posterior SD	Rhat	slope (truth 0.3)
  RW1 + cyclic seasonal, before	2.6101	7.1840	1.589	0.2886
  RW1 + cyclic seasonal, after	0.5054	0.0170	1.012	0.2878
  RW2, before	3.7200	1.1098	2.163	0.2926
  RW2, after	0.4868	0.0167	1.000	0.2907

  The slope is right either way, which is why nothing downstream flagged it.

• The spatiotemporal interaction’s margins are identified (#12). Same fit with a Knorr-Held Type IV interaction over 9 units x 5 times: the intercept posterior SD moves from 0.1106 to 0.0109 and Rhat from 3.43 to 1.14. It does not reach 1.05, and a 4x longer run makes it worse rather than better; that residual is tracked separately as #24.

• The cyclic RW rank is no longer overcounted (#12). RW1 and RW2 ranks were computed inline as cyclic ? T : T-1 / T-2 at 16 sites. A cycle-graph Laplacian still annihilates the constant, so a cyclic RW1 has rank T-1, and a cyclic RW2 T-1 as well (a linear ramp is not periodic). Every site now calls tulpa::rw1_rank() / rw2_rank() from the engine, which cannot drift from the engine’s own normalizers.

• The spatiotemporal analytic gradient matches the penalty it differentiates. Three further 0.001 literals sat in the ST gradient paths, including the one feeding the fused log-posterior reconstruction. With the old constant the resulting mismatch was too small to see; at the correct precision test-gradient-correctness.R fails on icar_st and bym2_st until the gradient carries the same per-margin constants. hmc_sampler.h’s build_and_factorize() keeps its 0.001, renamed diag_ridge: it is a Tikhonov ridge lambda*I that makes a Kronecker of two intrinsic precisions factorize, not the rank-1-per-margin sum-to-zero penalty.

• The multiscale, TVC and SVC sum-to-zero terms are written once. The double and autodiff twins of the multiscale kernels, tvc_sum_to_zero_penalty and svc_sum_to_zero_penalty are collapsed into one body templated over the scalar type. The missing multiscale pin survived a previous migration pass precisely because it had to be found in six places; there is now one. src/soft_sum_to_zero.h is gone in favour of the engine’s tulpa/soft_sum_to_zero.h.

• Recovery tests cover the fields that were unconstrained. test-recovery-constrained-fields.R gains multiscale (RW1 trend + cyclic seasonal, and RW2 trend), TVC and spatiotemporal cases. Shape and class assertions pass just as happily when a field’s level is free, which is how all three items reached a release. The TVC and spatiotemporal cases assert recovery but not Rhat: neither model mixes to 1.05, which reproduces on the released build for TVC (#23) and is a separate residual for the interaction (#24). Both comments say so and name the issue to restore them under.

tulpaRatio 1.4.1

• The spec path’s ICAR / BYM2 field is identified (#19). The bridge that builds the engine’s ModelData assigned the CSR adjacency without the component partition the sum-to-zero augmentation iterates over, so the field’s constant direction carried no precision and its level random-walked: mean(phi_spatial) reached +200.9, -342.8 and +423.1 at seeds 42, 43 and 44 where the legacy backend holds it within 0.02 of zero, and tau_spatial came out at 16.89 against the legacy 11.90. The field’s shape and the fixed effects were unaffected, which is why it read as a healthy fit. The bridge now calls ModelData::set_spatial_adjacency(), which derives the partition and the component count from the adjacency (tulpa 0.0.108). The two B1d spec-vs-legacy parity tests run again, having been skipped since the defect was filed.

tulpaRatio 1.4.0

• tratio() replaces ratiod() as the package’s front door, joining the tulpa* family of fitting verbs alongside tulpa::tulpa() and tulpaObs::tobs(). The ratiod_* family constructors, the ratiod_fit class, and every other ratiod_-prefixed name are unchanged.

• tratio() carries statistical arguments in its signature and every perf, numerical, and tuning knob in a single control = list(), matching the convention the engine and tulpaObs already follow. Moved into control: chains, iter, warmup, thin, cores, seed, verbose, adapt_delta, max_treedepth, metric, riemannian, gradient_mode, re_param, vi_variant, and the stochastic-gradient knobs (batch_size, epsilon, alpha, schedule_*, use_schedule) that previously arrived through .... An unrecognised knob is now an error rather than a silent no-op, and a warmup that would leave no post-warmup draws is rejected.

• refresh is removed. It was a documented argument that no backend ever read, so passing it changed nothing.

Bug fixes

• Spatiotemporal models fitted under an autodiff gradient mode now sample the density they are meant to (#14). The log posterior is written twice, once as compute_log_post and once as the templated compute_log_post_impl that the arena, forward, and tape modes differentiate, and the spatiotemporal interaction was present only in the first. The interaction parameters therefore carried no prior and contributed nothing to either linear predictor, so their gradient was exactly zero and every other parameter was differentiated against a linear predictor short one effect. The interaction now enters the templated log posterior as well, covering the Knorr-Held types I to IV, the separable and non-separable GP forms, the HSGP basis form, and the non-centred Type IV parameterization. The interaction math in src/hmc_spatiotemporal.h is templated in place rather than copied into a second autodiff header, so both paths call one implementation. gradient_mode = "handcoded" was correct throughout and is unchanged, to the last bit.

• The samplers no longer hold their per-thread scratch in the shape that corrupts the heap on Windows (#16). Every workspace in hmc_sampler.cpp was a static thread_local object owning heap buffers. Under the mingw toolchain such an object gets emutls-backed storage and a destructor registered through __cxa_thread_atexit, and the two are released by thread-exit hooks whose relative order is unspecified, so the destructor can free through storage that is already gone and the process dies at some later unrelated free. A worker thread exits only when libgomp narrows a team, which is why the fault appeared when a fit asked for fewer chains than the one before it. The 42 declarations now go through RATIOD_TLS_WORKSPACE (src/tls_workspace.h), which keeps the workspace behind a constant-initialized pointer: no initialization guard, no destructor registration, nothing for a dying worker to free. No object in the package registers a thread-exit destructor any more.

• tratio() no longer fails against a tulpa version the requirement allows. It calls tulpa::tulpa_check_control(), exported from tulpa 0.0.99, while DESCRIPTION asked only for tulpa (>= 0.0.95).

tulpaRatio 1.3.1

• diagnostics() replaces mcmc_diagnostics(), following the engine rename in tulpa 0.0.95. It delegates to tulpa::diagnostics(), which selects chain or approximation diagnostics from the fit’s draws provenance rather than from the function’s name. mcmc_diagnostics() is deprecated and still returns the same value.

Bug fixes

• A fit no longer decides how many threads later fits get (#16). The Laplace and Polya-Gamma backends take a per-fit cores, which they applied by moving the process-wide OpenMP thread count and leaving it moved, and it defaults to one thread. Every region in the session that read that value afterwards was therefore sized by whichever of those fits ran last, in fits it knew nothing about. Each backend now restores the previous value when it returns, on the interrupt path as well as the normal one.

• Successive fits in one session no longer corrupt the heap on Windows (#8). The OpenMP team for chain-parallel work was sized per fit from cores, so a fit asking for fewer cores than the one before it shrank the team; libgomp then destroyed the surplus workers and ran their thread_local destructors while later work was still in flight, and the damage surfaced as STATUS_HEAP_CORRUPTION at an unrelated free(). Four chains followed by two was enough to trigger it. The team now only ever grows, sized by the widest core budget any fit has asked for, and cores bounds how many chains are in flight instead, so it keeps its meaning. Growing a team was always safe; only shrinking faulted. Its size never comes from omp_get_max_threads(): the Laplace and Polya-Gamma backends move that value through omp_set_num_threads() and leave it moved, so reading it let a Laplace fit ahead of the first chain fit pin the team at one thread and serialize the chains of every fit after it.

• The Gibbs spatial backend runs its chains in parallel under OpenMP (#7). It looped over chains in R, so a 4-chain fit cost close to four times a 1-chain fit; on a 40-site binomial ICAR model that ratio drops from 3.7x to 0.94x. cores now reaches this backend, bounded by the chain count and the machine. Chain seeds are still derived in R, so a fit with a given seed returns the same draws as before, and the parallel and serial paths agree bitwise.

• The handcoded gradient no longer disagrees with its own log posterior when a temporal effect is not shared between numerator and denominator. The vectorized and fused kernels added the temporal effect to the denominator’s linear predictor and fed the denominator residual back into the temporal gradient, both unconditionally, while compute_log_post() applied the effect to the numerator alone. NUTS therefore sampled a density whose gradient pointed elsewhere, silently, for every family with a real denominator (poisson_gamma, negbin_negbin, negbin_gamma, gamma_gamma, lognormal). Binomial models were unaffected. The gradient checks now cover a continuous-denominator family with shared = FALSE, which is the only configuration that can see this.

• ModelData’s scalar members are initialised at declaration. The constructor set only unique_id, so roughly thirty members held indeterminate values until assigned; the GP entry point never assigned re_parameterization and read stack residue to choose between the centred and non-centred parameterisation.

• The scalar gradient fallback reduces its per-thread partial sums in thread-index order rather than in a critical section, whose summation order varies from run to run.

• mcmc_diagnostics() now reports per-parameter Rhat and ESS (#4). It handed a multi-parameter draws array to the single-variable posterior::rhat() / ess_bulk() / ess_tail(), collapsing every parameter to one scalar that was then recycled across the returned rows: a well-mixed four-chain fit reported rhat = 2.124, ess_bulk = 1. Diagnostics now delegate to tulpa::mcmc_diagnostics(), the engine’s per-parameter implementation, which removes the local re-derivation (compute_diagnostics_basic(), compute_split_rhat(), compute_ess_basic()). summary(), plot_rhat(), plot_ess(), diagnostic_summary() and check_diagnostics() all inherit the correction, and a converged fit no longer prints a false non-convergence warning.

• print() on a ratiod_diagnostic_summary no longer errors when a fit has a parameter with Rhat > 1.01 or ESS < 400. The worst-Rhat and worst-ESS tables are column extracts of the diagnostics table and dispatched on print.ratiod_diagnostics(), which rounded columns the extract does not carry.

Internal

• B1a PoC: plain binomial fits can route through tulpa’s LikelihoodSpec path (tulpa::get_nuts_fn()) behind a feature flag (options(tulpaRatio.use_specs = TRUE) or TULPARATIO_USE_SPECS=1). Off by default; legacy backend remains the production path until full parity ships in B5. Posterior means agree with the legacy backend within Monte Carlo noise.

• B1b: the same feature flag now covers all 7 ratio families (binomial, poisson_gamma, negbin_gamma, negbin_negbin, gamma_gamma, lognormal, beta_binomial) for the simplest non-ZI / no-spatial / no-RE / single-chain configuration. Autodiff only — H-kernel port is deferred to B2.

• B1c: zero-inflation, hurdle, and one-inflation variants now route through the LikelihoodSpec path. Each family’s templated likelihood dispatches on data.zi_type and calls a shared mixture helper in src/lik_specs/lik_helpers.h; per-family allowlist lives in src/lik_dispatch.cpp and R/backend_hmc.R::SPEC_ZI_COMPAT. Posterior parity with the legacy backend matches within Monte Carlo noise for every binomial × {ZI, hurdle, OI, ZOIB} combination; for count families (poisson_gamma, negbin_*) the legacy A_r path was already silently ignoring ZI, so the spec path here gives the mathematically correct posterior — recorded as a behaviour change, not a parity mismatch (see dev_notes/B1c_zi_surface.md). Autodiff only; ZI variants for the hand-coded H-kernel remain deferred.

• The tulpa ABI test no longer pins a hardcoded version number. It compared cpp_tulpa_abi_version() against a literal 32L, a second copy of a constant tulpa already owns, so every tulpa ABI bump broke the test with a stale-literal failure that said nothing about compatibility. Two unguarded accessors (cpp_tulpa_compiled_abi_version(), the value baked in from the LinkingTo headers, and cpp_tulpa_runtime_abi_version(), the value in the loaded tulpa DLL) let the test assert the invariant that matters — that the two agree — and report both numbers when they do not. tulpa remains the single source of truth for the version.

• Documentation is regenerated under roxygen2 8.0.0, matching the rest of the packages that link against tulpa. RoxygenNote migrates to Config/roxygen2/version. Two external links now resolve to a topic alias rather than an Rd filename (posterior::as_draws_df(), loo::stacking_weights()), which clears the “Non-topic package-anchored link(s)” note from R CMD check; INFERENCE_TIERS drops the \docType{data} and \format{} entries roxygen2 8.0.0 no longer emits for documented values; and the package page lists authors alongside the maintainer.