Changelog

Source: NEWS.md

dmsa 0.99.2

• Direction calls now ship with the package. dmsa_align() accepts a plain character vector of CpG identifiers and takes both the direction calls and the probe-to-gene mapping from a bundled map, so a user who has just finished normalising can align without obtaining a direction resource first. That round trip was the largest barrier between a normalised matrix and a DMSA result, and it is now gone for the common case.

  The bundled layer is blood, restricted to probe-gene pairs carrying an actual call: 488,204 pairs over 237,761 probes and 5,376 genes, in 1.3 MB. About seven in ten rows of the full table abstain, and an abstention cannot be aligned to, which is why the reduction costs nothing DMSA could have used. Other tissues, the brain bridge, the SMR layer and the full 3.1M-pair table remain in cpgdirection (), which stays the complete resource. Supplying your own table works exactly as before.

• dmsa_directions() exposes the bundled map directly, for checking what fraction of a probe set carries a call before committing to an analysis.

• Every alignment records which direction map produced it, in the "direction_map" attribute. Annotation drift silently changes enrichment results, and a direction map is an annotation; a DMSA finding that moved because the map moved would be the failure this package exists to name. The map is versioned and the version travels with the result.

dmsa 0.99.1

• dmsa_pdance() is now part of the package. The p-dance test - does a finding’s p-value move when the analyst changes which other probes are in the analysis? - existed only as an analysis script behind the manuscript’s Figure 3, while the README advertised it as a function. It is now exported, documented and tested, in both forms: "dropout" removes a random fraction of the non-focal probes, "addition" adds probes from a reservoir outside the set. The two move competitive and whole-set engines in opposite directions, which is why running only one understates the problem.

  It is engine-agnostic by construction: an engine is any function from a set of probes to a p-value, so a competitor is tested by exactly the procedure that tests DMSA. The focal unit’s own probes are never perturbed, so any movement is attributable to the analysis set rather than to the evidence.

dmsa 0.99.0

Bioconductor submission version. No change to any estimate, permutation, or correction; every number in the manuscript reproduces from v1.20.2, archived at 10.5281/zenodo.22042907.

Bioconductor requires new packages to enter review at 0.99.z and to release at 1.0.0, so the version number resets here. The GitHub and Zenodo lineage continues at 1.20.x; the concept DOI 10.5281/zenodo.22023957 resolves to whichever is current.

• cpgdirection is no longer a declared dependency. Bioconductor cannot install packages from GitHub, so it forbids declaring them even in Suggests. The dependency was already optional in fact — every call site guarded it and either named an alternative or degraded — so it is now resolved by name at call time through a documented .cpgd() helper. Behaviour is unchanged when the package is installed, and the error messages now name the repository and the DOI when it is not.
• biocViews added, and VignetteBuilder: knitr.
• A vignette, vignette("dmsa"): a worked analysis in which a direction-blind test reads null and DMSA recovers the signal from the same simulated data, with the cancellation constructed explicitly.
• NEWS.md now ships inside the package rather than being excluded from the build, and a stray log file was removed from the package root.

dmsa 1.20.1

Test-suite fixes only. No change to any result.

• test-frame.R asserted the literal word “binary” in a design note. Rewording that note in 1.19.0 - necessary, because “switched to logistic” wrongly implied a logistic model is fitted - therefore failed a test whose behaviour had not changed. The test now asserts the claim (a note exists for the outcome, it identifies a two-level outcome, and it describes a contrast) rather than the prose.
• Three legitimate warnings in the reference tests were surfacing as uncaptured warnings in the suite. They are now captured with expect_warning(), so a clean run reports no warnings.

dmsa 1.20.0

Three review items, all verified on real data.

• A binary outcome now gets its response curve. The shape scan used to skip a non-gaussian outcome entirely. The reasoning was half right: putting a 0/1 outcome on the left of a least-squares fit is a linear probability model, and the Sasabuchi and Fieller statistics are undefined on it. But a binary outcome IS non-linear - probability is bounded at 0 and 1 - and on the LOGIT scale the quadratic term is perfectly well defined. Curvature is now tested by likelihood-ratio against the linear logistic model and the fitted response curve is drawn on the probability scale, with observed proportions in equal-count bins carrying Wilson intervals, the data as a rug, and the same solid-inside-90% / dashed-beyond extrapolation rule as the other shape figures. On the Alpha parent build this surfaced curvature at p = .0062 that the old skip reported as nothing at all.

• A duplicate unit no longer survives on an uncorrected family of one. A gene with a single direction-called probe creates a PROBE family of one, where maxT has nothing to correct against. The identical measurement therefore came out at the raw p (.028) while its gene-level twin, corrected across a 6-gene family, failed (.108) - and because the probe was flagged a duplicate it was also excluded from the figures, so the run reported a survivor it never drew. Duplicates now inherit the adjusted p of the unit they duplicate, which is the multiplicity actually incurred; each unit’s own value is kept in a *_adj_own column so the inheritance is auditable.

• Legend collision in the moderation panels. The legend was drawn at “topleft” over a full-height y range and sat on the scatter and the slope lines. Headroom is now reserved and the legend has an opaque backing.

• The overview figure title used the raw outcome column name rather than the supplied label, and wrapped by character count rather than measured width.

dmsa 1.19.0

Five review comments from the Set 1 rev-3 output, all implemented.

• Categorical outcomes were described as if they were continuous. One sentence template said “Higher X was associated with …”, which produced “Higher sex was associated with …” and “Higher time was associated with …” in shipped output. Three cases are now told apart, and by the DATA rather than the column name - a two-level outcome that varies within the finest permutation block of a before/after build is a within-person change, one that does not is a group contrast:
  • continuous: “Higher X was associated with …” (unchanged)
  • binary: “Relative to female, male showed methylation consistent with …”
  • wave: “Within person, from T1 to T4, the methylation of MEG3 shifted toward a LOWER expression tone.” New outcome_levels = c("T1", "T4") names the two levels; without it they are named by their coded values, which is ugly but never ambiguous.
• The design note binary outcome under gaussian -> outcome_type switched to 'logistic' was misleading - methylation is the response and the outcome is a predictor of it, so no logistic model is fitted. It now says what actually happens.
• Labels reached exactly one place in the summary. With outcome_label set, the Results paragraph honoured it and the report header, the survivor list, the system line and the Methods paragraph all still printed the raw column name. All of them now use the label. Tables gain a unit_label column and keep unit as the join key; design notes keep raw names, because they refer to columns in the data file.
• Figure text was cut off. A module-level three-way title measured 15.25 in of text into 6.6 in of usable width - a 2.31x overflow, centred, so it was cut at both ends. Three separate causes: the p-values were concatenated into the title (they are now a subtitle); wrapping used a fixed CHARACTER count rather than measured width; and strwidth() measures against the panel’s par("cex") while mtext(outer = TRUE) draws at device scale, which under-measured every outer title by 1.2x in a 2x2 layout. Titles are now fitted before the layout is set, the outer margin is sized to the number of lines they need, and long labels shrink rather than clip. Also fixed a non-terminating loop in the truncation fallback.
• A single CpG is now named GENE's cgXXXXXXX in figure titles, figure file names, axis labels, the summary prose and the tables. A bare probe id does not say which gene’s level-local family it was corrected inside, and the family IS the gene.
• Moderated non-linear models gain a surface figure - 3D perspective plus a filled contour with the observed data drawn on it. Only for a moderated non-linear model: a plain shape scan has one predictor and therefore no second axis. Grid cells with no observation within one bandwidth are drawn grey (3D) or blank (contour), and the figure prints what fraction of the plotted surface is extrapolation - a quadratic-by-moderator surface extrapolates violently at the corners, and a 3D surface makes that look like data.

dmsa 1.18.0

Chip now enters as a random intercept, as the Project Alpha covariate contract specifies.

tutor/covariate_sets.csv states 1 parent T1, chip_T1, random, (1 | chip_T1). Through 1.17.0 dmsa entered chip as a FIXED factor - 61 levels, 60 df on the parent build, with 7 singleton chips fitted perfectly. That was an undocumented deviation from the pre-specified contract.

Why chip is needed at all (parent build, n = 396):

• chip is not associated with any outcome (F ~ 1, p = .25-.73 across five outcomes), so it is a precision variable, not a confounder - omitting it cannot bias an effect estimate;
• but it is a large technical-variance component in methylation: 61% of probes show chip signal beyond the control-probe SVs at p < .05 (5% expected) and 30% at p < .001 (0.1% expected);
• the control-probe SVs do not substitute for it (ctrlSV3 R2 = .32, ctrlSV5 R2 = .36 against chip);
• families span 2-3 chips, so chip is crossed with the cID permutation block and blocking on cID does not absorb it.

Implementation: for a one-way random intercept the GLS solution is exactly OLS on quasi-demeaned data (Balestra-Nerlove), with the variance ratio estimated by REML. No mixed-model package is required - the REML step is a 1-D optimisation in R/ranef.R. Validated against lme4::lmer(REML = TRUE) on the Alpha parent build: coefficients and t-statistics agree to 5e-13, variance components to 4e-06, including the sigma^2_u = 0 boundary.

Because the transform is a linear map applied once, before any permutation, Freedman-Lane block permutation is unaffected. Validity does not depend on the variance ratio being exactly right: the null is generated under the same transform, so the variance ratio affects power, not type-I error.

What it costs and what it buys: the random intercept spends a median of ~16 effective df instead of 60, and 0 on the ~20% of probes where the chip variance is estimated as nil. On the two deposit genes it is appropriately more conservative than the fixed factor, and both still survive:

gene	random (contract)	fixed (1.17.0)	chip omitted
PLAGL1	adj .0260 survives	adj .0100 survives	adj .1360
AVP	adj .0140 survives	adj .0080 survives	adj .0140 survives

Omitting chip loses PLAGL1 altogether.

• New argument chip_effect = c("random", "fixed", "none"), default "random". "fixed" reproduces <= 1.17.0 behaviour and is ~5x faster, which is useful for structural test runs; the Design notes now record which was used and flag "fixed" as a deviation from the contract.
• Fixed a silent bug introduced with this change: under "random" the chip column was dropped from the retained data, which would have left the grouping vector NULL and the random intercept never applied.

dmsa 1.17.0

Five defects found by auditing a 29-model battery run on all four Project Alpha builds. All five were silent: every one produced a report that compiled cleanly and said something false or incomplete.

• Surviving modules were never named in the report. hits.csv carried them, the summary and the “How to report this” Results block did not. In one run four modules survived, the strongest at adjusted p = 0.002 - better than two of the three genes that were reported - and a reader of summary.md alone would never have known. Module survivors are now listed in the summary and get a Results paragraph, with the caveat that a module pools several genes. "nothing survived" is also no longer printed when a module did.

• The significance star at system level marked the wrong quantity. The star came from selected, which is driven by the best lens’s adjusted p, but it was printed beside the ACAT omnibus p. That produced omnibus 0.0658 * and omnibus 0.0820 * - a significance mark on a p above alpha. The line now reads omnibus 0.0820 (best diffuse adj 0.0220 *), so each star sits on the number it refers to.

• The main-effect Methods block never stated the covariates. The moderated block always did. A reader of a linear report could not tell what had been adjusted for - including in runs where a mistyped covariate was silently dropped, or where every covariate passed was unknown and the model was effectively unadjusted. The Methods text now names the covariates, the block structure, and warns explicitly when requested covariates were dropped.

• “Coverage: 0 gene(s) … untestable” in three of the four builds. The untestable-gene count re-read the direction map from disk, so it still held parent-T1 column names and matched nothing in the child and before-after builds. Those reports claimed complete coverage while the parent build, on the same two systems and the same 18 genes, correctly reported 36 untestable. The raw map is now translated to the build before the intersection.

• The console under-reported the table count by one whenever probes.csv was written; it was never added to the tally.

dmsa 1.16.0 (2026-08-19)

Everything in this release came out of running the 25-model Set 1 battery on all four Project Alpha 2026d builds. Five of the twenty-five models failed and two more passed while testing something other than what they claimed to.

Three of the four builds could not be analysed at all (critical)

• The direction map names probes in the parent-T1 form only. The same CpG is cg02187522_TC21_T1_AVP in the parent build, cg02187522_TC21_AVP in the two before/after builds, and cg02187522_TC21_T4_AVP (plus _maternal_T1_ and _paternal_T1_ sets) in the child build. dmsa_frame() intersected the map’s column names with the data’s and found zero matches for the child, T1-T4 and Oct-7 builds, stopping with “no mapped methylation columns found”. The selection cascade lists every variant explicitly (col_parent_T1, col_long, col_child_T4, col_child_maternal_T1, col_child_paternal_T1), so the frame now translates the map to whichever form the data uses, prefers the variant belonging to the detected build when several match (the child workbook carries three), and records the translation in the corrections table. Passing methylation as an explicit vector of column names overrides the choice - that is how to analyse the maternal or paternal array inside the child workbook.

Typing the contract out by hand gave a different model (serious)

• sex_c was derived only under covariates = "contract". A caller who passed the same six columns explicitly got sex_c: covariate absent from data -> dropped, so the model was silently not adjusted for sex and returned different p-values from the identical-looking contract call. sex_c is a derived column, not a column of any workbook, and is now built whenever it is asked for by either route.
• A missing covariate now raises a warning(), not just a line in the corrections table. A mistyped covariate name is indistinguishable from an intended one, and dropping it quietly produces an unadjusted model that still reads as adjusted in the write-up.

Moderated curvature was reported in prose and never drawn

• moderation$selected was keyed on the linear product’s adjusted p alone. A run whose only survivors were moderated curvature (S^2 x moderator) named nine of them in the summary and produced no figure at all, and any user filtering moderation$selected saw none of them. The table now carries curv_selected and any_selected, and the figure loop draws both arms, skipping units flagged as repeating the same data at another level.
• The figure title now leads with the arm that actually carried the unit. A panel drawn for a curvature survivor used to be headed “family-adjusted p = 1.0000”.
• selected is now guarded with is.finite(); an NA adjusted p produced NA rows.

chip is an argument

• dmsa_frame(chip = FALSE) leaves the array batch factor out; a character string names the column to use; TRUE (default) keeps the previous auto-detection. On the parent build chip_T1 has 61 levels across ~400 arrays with 7 singleton chips, so entering it spends 60 degrees of freedom on a nuisance factor that the control-probe surrogate variables already partly absorb. That is a judgement to be made and reported, so it had to be sayable. The before/after contracts forbid chip and still override the argument.

Shared probes: audited, and the hazard is structurally absent

• 229 probes in the Alpha panel sit inside two panel genes and appear as a column under each name (IGF2+INS 31, GABRA5+GABRB3 25, HLA-DPA1+HLA-DPB1 23, PEG3+ZIM2 21, and others). Two such genes in one level-local family are not two independent units. .frame_shared_probes() now reads the cascade’s explicit shared_with column rather than trying to infer sharing from duplicated probe names - which cannot work, because an Alpha column name embeds its gene, so one probe under two genes is two differently named columns.
• Auditing all 494 documented (probe, gene, partner) rows against the direction map: not one shared probe survives into the map under both names (62 under exactly one, 432 under neither). No DMSA analysis of this panel can double-count a shared probe. The check is retained as a guard for future maps and reports into frame$shared_probes.

Two failures that gave uninformative errors

• A selected system left with no direction-called probe reached the aligner with a zero-row table and died inside it on replacement has 1 row, data has 0, which says nothing about what went wrong. It now stops with the system name, how many probes remain overall, and which systems they cover.
• When every mapped probe column has a missing value on the complete-case rows the frame now says so, and names the usual cause: the methylation set and the covariates were measured on different people. That is exactly the case in the Project Alpha child workbook, which carries the mother’s and father’s T1 arrays but no maternal or paternal cell-composition or control-probe covariates - so those arrays cannot be analysed with the child T4 contract, and the frame now says that instead of failing obscurely.

Smaller

• Moderation figure captions are wrapped to their panel (at most two lines). The four-line model statement on the left collided with the legend; a single unwrapped line then ran into the neighbouring panel’s caption in the three-way layout and the Johnson-Neyman note ran off the panel edge. The full model statement belongs in the summary.
• Captions name the moderator by its display label throughout; one line still used the raw column name.
• The shape-scan summary said figures are drawn “for units whose adjusted quadratic p clears alpha”. They are drawn for any surviving departure arm - quadratic, threshold or exponential. The sentence now says what the code does.
• The “no figures” message for a moderation run names both arms when type != "linear".
• dmsa_frame(chip = "pool"), see above.
• “1 direction-called probes” now agrees in number.

dmsa 1.15.0 (2026-08-18)

covariates = "contract" is build-aware (important)

• The contract was hard-coded to the parent T1 build. Project Alpha ships four builds, each documenting a different covariate set in its own Covariates sheet. .frame_contract() returned the parent T1 set for all of them, so on the other three every contract column except sex was absent, silently dropped as “covariate absent from data”, and the model ran on sex alone. On the two before/after builds that omits time - the design itself. The contract is now selected from the columns present: parent T1, child T4, or before-after. When no documented contract matches, it errors instead of degrading.

• Repeated-measures builds get the right blocks. The before/after builds list ID and cID as random effects. Permuting within cID alone would let two timepoints swap between members of a twin pair, breaking the within-person pairing; when the caller leaves random_effects at its default, those builds now block on c("ID", "cID") and say so.

• chip is not entered where the contract forbids it. The before/after builds list chip as NEVER include; it is now omitted there rather than added as a fixed factor.

• The build and its contract are printed in the frame’s corrections table, so the model’s covariate set is auditable from the output.

Overview figure fixes

• “Bold unit = survives” was wrong in every overview figure. axis() takes a single font, not a vector, so ifelse(selected, 2, 1) applied the first element to every label: all units bold when the top row survived, none otherwise. Drawn with mtext() now, which vectorises.
• A two-line wrapped title was clipped at the top of the device.

dmsa 1.14.0 (2026-08-18)

The summary is what people will read, so it now says what it means

• A family of one is marked as uncorrected. At probe level a gene with a single direction-called CpG forms a family of one, where maxT has nothing to correct against and the “family-adjusted” p equals the raw permutation p. That number was being reported alongside genuinely corrected ones - and because it is the smallest, it looked like the strongest result in the run. The summary, the “How to report this” sentence and print() now all say FAMILY OF 1, so uncorrected.

• The same data at three level names is no longer counted three times. A module whose only direction-called gene is X, that gene, and (if it has one probe) that probe produce identical statistics. They were listed as separate survivors, multiplying the apparent number of findings: “6 survive” was really 4 distinct results, and “2 survive” was 1. Duplicates are now detected by identical outcome, b and t, labelled = <coarser unit>, and a distinct count is given.

• The Methods paragraph covers the moderated non-linear model. It described only the two-way product, so a reader copying it would not know S^2 had ever been fitted. It now states the second model and its tested term.

• The curvature paragraph interprets its sign - positive means the curve becomes more convex as the moderator rises, negative more concave - instead of listing coefficients with no reading.

• Curvature panels no longer say “Johnson-Neyman: slope”. The no-boundary branch kept the slope wording over a curvature axis.

• Labels reach the rest of the report: the moderation and curvature paragraphs, panel subtitles and the J-N/curvature axis were still printing raw column names while the titles used the labels.

• Singular “1 direction-called CpG”.

dmsa 1.13.0 (2026-08-18)

Outcome types, labels, and the non-linear moderation figure

• A nominal outcome is now refused instead of silently scored (bug). The main path regresses methylation ON the outcome and tests a single 1-df coefficient. With outcome_type = "multinomial" a k-level nominal variable was coerced to numeric and analysed as an ordered, equally-spaced score - “delivery mode 5 is five times delivery mode 1”. dmsa_frame() now errors and points at dmsa_outcome() (which implements a rank-based scheme for multinomial and ordinal outcomes) or a binary recode. The gaussian-with-few- levels note now says the variable is being treated as ordered.

• outcome_type no longer implies a link function that does not exist. The frame printed “model: logistic linear” while fitting OLS. It now states that the outcome enters as a PREDICTOR of methylation, so a binary outcome is a group contrast on a continuous response and no link function is needed or used. That is correct for the main path - and the shape scan, which puts the outcome on the LEFT, now refuses a non-gaussian outcome rather than fitting a linear probability model under Sasabuchi/Fieller machinery.

• A covariate derived from an outcome is excluded up front (bug). With outcome = "sex" the contract built sex_c from sex, then found the design rank-deficient, then dropped it - three messages leaving the user to work out that the covariate WAS the outcome. It is now excluded before fitting, with that stated as the reason. (The final model was correct either way.)

• The non-linear moderation figure draws the non-linear model. With type != "linear" the panel drew straight simple slopes while the table reported moderated curvature. It now fits and draws the curved model, the right-hand panel shows CURVATURE as a function of the moderator (J-N is about a slope, and with curvature the slope depends on the score too), and the title carries the moderated-curvature adjusted p.

• Three-way panels name their mod2 level. The row label lived only in the outer note, so each panel was ambiguous on its own.

• Display labels. dmsa_frame() gains outcome_label, predictor_labels, mod_label and mod2_label - one per column, in order, or a named vector. They appear in figures and in the written report in place of raw column names. Purely cosmetic: no label reaches the model, the permutation or the correction.

dmsa 1.12.0 (2026-08-18)

Moderated non-linear is now a real test, and the shape prose reaches the report

• type was silently ignored under moderation = TRUE (bug). The moderation branch of dmsa_report() returns before the shape scan runs, so asking for a moderated non-linear model produced a moderated LINEAR one with no warning. There is now an actual moderated non-linear test: whether the CURVATURE of the tone score depends on the moderator, tested as the moderator by squared-score product with S, S^2, the moderator and S x moderator all present for marginality, maxT-corrected within the same level-local family. On the Alpha HPA/SNS panel 6 of 106 units survive it.

• “How to report this” now covers the shape scan. It was main-effect only, so a run with type = "non-linear" handed the user a Methods paragraph describing a purely linear analysis - which is what they would then publish. There is now a Results-shape paragraph stating the incremental test and its adjusted p, a Methods-shape paragraph, and a reference list (Fieller 1943; Ganzach 1997; Lind & Mehlum 2010; Morris et al. 2023; Sasabuchi 1980; Simonsohn 2018). The linear Methods paragraph now says outright that the unit-level tests are linear in the aligned score.

• The shape table ranked by the quadratic arm only (bug). Units surviving on the threshold or exponential arm were pushed off the bottom of the truncated table while still getting a figure, so the table and the figures disagreed. Ranking is now by the best adjusted departure p across arms, and any surviving unit is always shown.

• A family of one is marked as such. At probe level a gene with a single direction-called probe forms a family of one, where maxT cannot correct anything and the “adjusted” p equals the raw p. The table now says so on the row instead of presenting an uncorrected number as corrected.

dmsa 1.11.0 (2026-08-18)

The scan now covers the whole declared hierarchy, and the exponential arm is real

• Probe level added, so system > module > gene > probe is true. .rp_units() had no probe branch, so the shape scan and the moderated path both stopped silently at the gene level while the declared design said otherwise. Only the main-effect path ever reached probes. There is now a probe branch, with family = the probes of one gene, matching the family the main-effect path corrects within. Both the shape scan and moderation use it. On the Alpha HPA/SNS panel the scan goes from 22 units to 53: 2 systems, 9 modules, 11 genes, 31 probes.

• Every departure arm is maxT-corrected, not just the quadratic. An type = "exponential" run reported its own arm raw while the quadratic beside it in the same table was corrected. Each arm now gets Westfall-Young maxT inside its level-local family on its own permutation stream.

• The figure gate no longer keys on the quadratic alone. A run whose exponential arm survived drew no figure, because the gate asked only whether the quadratic had survived. It now fires on any surviving departure arm, and the exponential fit is drawn and carries its p-values in the legend.

• The panel carries numbers; the summary carries the argument. The figure header had grown to seven lines of prose that collided with the legend. It is now three short lines - unit, shape verdict with the Fieller interval, and the solid/dashed convention - with every arm’s raw and adjusted p in a single legend. The reasoning about marginality, Sasabuchi and Fieller stays in summary.md where it can be read properly.

• A caution on the exponential arm. exp(score) is a monotone transform that correlates strongly with the linear score, so the incremental test has low power and a null there is weak evidence. It asks whether the association accelerates, not whether it is non-monotone - only the shape test speaks to that. Stated in the summary whenever the arm is present.

dmsa 1.10.0 (2026-08-18)

The shape scan runs at every level, and is corrected like everything else

• Scanned at system, module and gene, not only system. An aligned tone score exists at every level of the cascade, so there was never a reason to ask about functional form at one level only. type = "non-linear" now scans each declared level.

• Westfall-Young maxT inside level-local families. The scan previously reported raw p-values and was corrected nowhere - the one part of DMSA sitting outside the error control the rest of the package is built on. That is untenable once the scan covers dozens of units. The quadratic departure term is now maxT-corrected within its level-local family on a shared permutation stream, exactly like every other DMSA statistic. It changes conclusions: in the Alpha HPA/SNS panel, ADRA2A’s quadratic term is p = .032 raw and p = .060 adjusted, so uncorrected it reads as a finding and corrected it does not.

• Fieller interval for the extremum. Lind & Mehlum present containment of a confidence interval for the turning point within the observed range as the test equivalent to the slope-sign test, and warn that the delta method is severely biased in finite samples. The scan now reports a Fieller 95% interval and whether it is contained. An unbounded interval - which is common, and which the delta method would hide behind a tidy-looking standard error - is reported as unbounded, because it means the turning point is not determined.

• Figures are drawn for units that survive their family, not for every scanned unit, and the run says so when nothing survives.

• Documented against the source literature. The Sasabuchi hypotheses, the max-of-one-sided-p convention, the interior-interval suggestion and the Fieller recommendation were each checked against Lind & Mehlum rather than reconstructed from memory.

dmsa 1.9.0 (2026-08-18)

The shape scan now respects marginality, and will not call a curve a U

Reported shape numbers change in this release. Anything taken from a 1.8.9 shape table came from the wrong specification and should be re-run.

• Departure terms are now fitted WITH the linear term (breaking). The scan substituted the transformed score for the linear score, so the quadratic arm fitted outcome ~ score^2 + covariates with no linear term. Because the score is standardised, that imposes a symmetric curve whose turning point is pinned at the mean, and a significant coefficient there is not evidence of curvature (the marginality principle; Morris et al. 2023). Each departure term is now entered alongside the linear term, so the reported p is the 1-df incremental test of curvature over and above the linear component - equivalent to comparing the linear and quadratic models directly. The threshold arm is fixed the same way.

• The omnibus no longer pools the linear arm. ACAT over {linear, quadratic, threshold} answered “is there any association”, which a significant linear term alone can drive and which therefore cannot support a claim about shape. The omnibus now combines the departure terms only and is reported as such, with the linear p in its own column.

• A U-shape claim now requires a U-shape test. A significant quadratic term is consistent with a relationship that rises throughout but with increasing steepness, so it does not establish a U or an inverted U (Lind & Mehlum 2010; Simonsohn 2018). The report adds the Sasabuchi slope-sign test - the fitted slope must take opposite signs at the two ends of the observed range, with the p-value taken as the LARGER of the two one-sided p-values - and declares a shape only when that test clears alpha and the turning point falls inside the observed range. Otherwise the summary says to report a curvilinear component and stop.

• A trimmed shape test, because the extremes are the sparse points. Lind & Mehlum evaluate the slope at the minimum and maximum of the observed range, which in a skewed score are exactly where the data thin out. The scan repeats the test on the central 90% and flags any shape that passes on the full range but not on the trimmed one, so a U resting on a handful of tail cases is visible as such.

• The figure draws the models its p-values came from. 1.8.9 drew a hierarchical quadratic curve while labelling it with the squared-term-only p-value. Every fit shown is now the same hierarchical model that produced the p beside it, the quadratic’s turning point is marked when it lies inside the data, and fits stay solid across the central 90% and dashed beyond.

• Two cautions written into the summary. The Sasabuchi test uses parametric standard errors rather than the block permutation, so it is anticonservative under clustering; and curvature in a multivariable model can be induced by an omitted product term (Ganzach 1997), so a curvilinear result is worth re-checking with the relevant interaction present.

dmsa 1.8.9 (2026-08-18)

Bug fix: type = "non-linear" computed a result and threw it away

• The shape scan was unreachable. With type = "non-linear" or "exponential", dmsa_report() ran an ACAT omnibus over linear, quadratic and threshold arms of each system’s aligned score - and then discarded it. The rows were assembled into a local list that nothing ever read. Consequences: a non-linear run returned a results table byte-identical to the linear run (verified: max absolute difference in p_omnibus = 0), the returned object had no shape component, and summary.md never mentioned that a scan had happened. A user asking for a non-linear analysis got a linear one with no warning. The scan now reaches summary.md, tables/shape.*, print() and report$shape, with the per-arm p-values that dmsa_model_omnibus() was already computing and .report_shape() was dropping.

• It says what it is, and what it is not. Each arm is fitted as the ONLY score term, so the scan asks which functional form of the whole-system score tracks the outcome - it is not a test of whether curvature adds anything to a linear fit, and it does not re-specify any unit-level test. The summary states that the system, module, gene and probe p-values remain the linear ones, so the scan cannot be mistaken for a non-linear model of the units.

• A figure per scanned system+outcome. The aligned score against the covariate-adjusted outcome, with the linear, quadratic and threshold arms drawn over the points and each arm’s own p-value in the legend. Fits are solid across the central 90% of the score and dashed beyond it, with the count of participants outside and a density strip along the axis: a quadratic’s tails are set by whatever few observations sit out there, and curvature carried by a handful of cases should not be able to look like the finding.

dmsa 1.8.8 (2026-08-18)

A surviving moderation now gets a figure, and the model gets stated

• Simple slopes with partial residuals, plus Johnson-Neyman. 1.8.7 said a moderation run draws nothing. That was the wrong fix for the right observation: an interaction that survives correction is precisely the result a reader cannot picture from a coefficient. Each surviving unit now gets moderation_<unit>_<outcome>.png - the tone-score slope at three levels of the moderator drawn over the partial residuals it was actually fitted on, and a Johnson-Neyman panel showing over what range of the moderator the slope is distinguishable from zero. A three-way run draws the pair twice, with the second moderator held at -1 SD and +1 SD.

• The J-N panel says how many participants are in the significant region. A boundary near the top of a skewed moderator can be perfectly real and still describe almost nobody. The subtitle gives the count and the percentage, and a density strip shows where the participants actually sit.

• The simple slopes are drawn at observed quantiles, not at +-1 SD. The +-1 SD convention puts a line at an impossible moderator value whenever the moderator is bounded and skewed - an adversity count has a floor at zero, and mean minus one SD is below it. The panel now uses the 10th, 50th and 90th percentiles, falling back down a ladder of quantiles when ties in a zero-inflated moderator collapse two of them onto the same value.

• The Methods paragraph now enumerates the model instead of gesturing at it. “All lower-order terms present” is not auditable. It now names every term in the fitted model, lists the covariates, states that with several outcomes each is additionally adjusted for the others, and says plainly that the cluster variable enters as a permutation block rather than as a random intercept - no mixed model is fitted, so there is no variance component to report.

dmsa 1.8.7 (2026-08-18)

Moderation runs get the same reporting support as main-effect runs

• summary.md now writes a “How to report this” section for mDMSA too. 1.8.6 added the section but returned early when there was no main-effect table, so the branch a user is most likely to need help wording - a slope-on-a-slope, reported from one collapsed score, under one lens - was the one branch that produced nothing. The moderated block states the three things the main-effect wording never has to: that the unit is collapsed into a single subject-level tone score BEFORE the interaction is fitted, that only the composite lens is reported because coherence and diffuse do not carry through a product, and what the coefficient means in words. With two moderators it says so correctly - the coefficient is the three-way term, so it reports how much the first moderator’s moderation depends on the second, rather than mislabelling it as a change in the simple slope.

• A moderation run with plots = TRUE says why it drew nothing. There is no per-probe effect and no three-lens panel once the unit is a single score. The run now names that, and names the switch that gets the figures back.

dmsa 1.8.6 (2026-08-18)

Reporting the result, not just computing it

• A dedicated module-level figure. modules_<outcome>.png plots every module in the selected systems across all three lenses on one family-adjusted scale. Modules are blocked and named by system, with a rule between blocks, because that boundary is the panel’s actual claim: a module is corrected against its own system’s modules and no others. Each row carries the module’s literature evidence tier and direction-called probe count in the right margin - Moderate rendered in red, so a result resting on a weaker module definition cannot be read without noticing. The module level was the one tier of the cascade with tables but no picture.

• summary.md gains a “How to report this” section. DMSA asks a reader to hold three unfamiliar things at once - which lens carried the finding, which family it was corrected inside, and a direction that is a claim about expression tone rather than about methylation level - and a user handed adj .0120, coherence, d = -1 has no way to know how to write that up. The report now writes the sentences, with this run’s own numbers in them: a Results paragraph per surviving unit, a Methods paragraph carrying the actual settings, and the direction stated in words. A run with no survivor gets the calibrated-null wording instead, which is the harder one to phrase.

Fixes

• Every table is also written as .docx, and if gt is missing the run says so once, up front, instead of leaving you to notice the absent Word file when you go to paste it. csv to compute on, html to read, docx to paste into a manuscript - a results table retyped by hand is a table with typos in it.
• The overview figure’s title no longer runs off the right edge. Outcome names are the user’s own column names, and “Attachment_Anxiety_General_T1” already overran the device. The title wraps to the panel width.
• A locus panel drawn without a gene model says so on the console, naming the argument that fixes it. The caption always said it, but captions get skimmed, and a panel with no exons is half the point of the panel.

dmsa 1.8.5 (2026-08-18)

• dmsa_report() now says where it wrote, every run, with the path resolved: outdir stays relative by default - the working directory is where every other R tool puts results, and changing that would be the more confusing choice - but “dmsa_output” is only findable if you already know what it is relative to. The message appears whether or not the result is printed, so assigning rep <- dmsa_report(fr) no longer ends a 35-minute run in silence. print() resolves the path too.

dmsa 1.8.4 (2026-08-18)

• The progress bar advances through the probe level and the figure/table phases instead of sitting at 71% and jumping to 100% at the end. The two trailing phases were counted in the total but never ticked.

dmsa 1.8.3 (2026-08-18)

Two conveniences for long runs

A report is minutes of permutation with nothing on screen - the parent cohort at B = 1999 across 19 systems takes 35 minutes - which leaves no way to tell “working” from “hung”.

• A 0-100% progress bar. dmsa_frame(progress = ), or override at run time with dmsa_report(frame, progress = TRUE). It counts triangulation calls, the unit of work that actually costs time, so the bar is proportional rather than a decorative sweep.
• A completion sound. dmsa_frame(beep = ). TRUE uses beepr sound 8, a number picks another, FALSE is silent.

Both default to interactive(). A progress bar writes carriage returns, which turn a piped log into soup, and a package that makes noise during R CMD check or a testthat run is one people learn to mute - so both are live at the console and off under Rscript, check and tests. beepr is a Suggests: without it the sound is silently a no-op, never load-bearing.

dmsa 1.8.2 (2026-08-18)

Regression fix: maxT had lost its information advantage under a single engine

Caught by running the shipped package against the pre-registered proof of concept and comparing every number to the deposited results.

A change on 15 August 2026 standardised each lens statistic by its own permutation null before the family-wise step. That was introduced to fix a real problem in the combined engine - a rank-p fusion floors the family-wise p near (family size)/(B+1), which silently zeroes power for large families at modest B - but it was applied to the single-engine path too, where it does not belong.

Standardising divides out exactly what maxT’s advantage is made of. A unit whose probes all point the same way generates the widest permutation null in its family and is therefore charged the least; rescale every unit to unit spread and that width buys nothing, leaving minP-like behaviour under a maxT label.

The cost was the pre-registered anchor. AVP’s family-adjusted coherence moved from .0120 - the reported value, and the one the p-dancing figures quote at every perturbation - to .1135, which is the documented combined-engine value of .125. PLAGL1, CAT and the exploratory sweep all shifted with it, in both directions, and a gene that is not a finding (ADCY6) appeared to clear .05.

Standardisation now happens only where two engines are actually fused. With one engine, maxT sees the raw statistic again.

Every number in the pre-registered proof of concept now reproduces exactly from the shipped package at weighting = "flat", cpg_map = "full", B = 1999: AVP coherence .0120/.0120, composite .0070/.1285, diffuse .0080/.0505; PLAGL1 coherence .0055/.0425, composite .0025/.0080, diffuse .0025/.1720. All twelve raw and adjusted values match the deposit.

Three tests now pin the property: the concordant unit’s toll stays small under one engine, maxT stays strictly kinder than minP to it, and the two-engine fusion keeps its standardisation.

dmsa 1.8.1 (2026-08-18)

The locus panel was mixing genome builds, silently

Found by running the whole pipeline on the real parent cohort rather than on test fixtures - which is the only way this class of defect surfaces, because neither layer is wrong on its own.

• dmsa_report() took probe coordinates from dmsa_probe_coords(), which reads the companion package’s EPIC manifest in hg19, and drew them against an hg38 gene model. AVP moves 19,353 bp between the two builds, so its eight promoter CpGs were drawn 19 kb from their own gene, in the middle of the neighbouring intergenic space, with nothing reported. Coordinates now come from the bundled cascade, looked up by probe id: it is hg38, it is the same table the gene models are aligned to, and it is always present. The manifest lookup remains as the fallback only.
• A genome-build guard. dmsa_plot_locus() now measures the distance from the probes to the gene model and refuses to pass it off as a map when they cannot belong together: if every probe is further from the gene than the gene is long (floor 5 kb), it messages naming the build and prints CHECK GENOME BUILD on the figure itself. The rule is distance, not overlap, because real promoter probes legitimately sit outside the gene span - all eight of AVP’s do - and a naive containment test would cry wolf on exactly the figure this feature exists to draw.

554 unit tests.

dmsa 1.8.0 (2026-08-18)

The locus panel draws the gene, not a bar shaped like one

Strip B used to draw a gene as a featureless grey rectangle, and - when a probe carried no coordinate - to space probes evenly along it. Both told the reader nothing while looking like they told them something. A CpG in the first exon and a CpG 40 kb into an intron are different claims about regulation, and which one it is decides the probe’s direction call. The panel now shows it.

• dmsa_gene_model() returns real exon, UTR and strand structure on genomic coordinates, from any of four sources in one shape: Ensembl REST (no install, no annotation package, cached to disk), a GTF/GFF3 you already have, a TxDb plus OrgDb, or an EnsDb. Every row records which.
• dmsa_plot_locus(gene_model = ...) draws it: coding exons as full-height boxes, UTRs half-height, introns as a line with chevrons running 5’ to 3’, and the TSS marked. These are the conventions every genome browser uses, so a reader already knows how to read the strip. transcripts = "all" stacks every isoform; the default draws the annotation’s own canonical transcript and names it under the axis.
• dmsa_plot_locus_gviz() hands the same probes and the same model to Gviz for what Gviz gives and this does not: cytoband ideograms, transcript collapsing, and any other track stacked in the same coordinate frame.
• dmsa_frame(gene_models = TRUE) turns the models on for every locus panel in a report. Off by default, because a report should not make network calls without being asked; pass a prebuilt table instead to stay offline.
• dmsa_gene_model_check() validates a model before it is drawn, including the failure this is really guarding against: a region query keeps the NEIGHBOUR gene’s exons and draws them as if they were this gene’s.

Gviz, GenomicRanges, IRanges, GenomicFeatures, ensembldb, AnnotationDbi and AnnotationFilter are Suggests. The Ensembl and GFF sources need none of them - nothing beyond base R - so the figure is not reachable only by users who can install a gigabyte of annotation.

The evenly-spaced fallback is no longer a silent default

It looks like a map and is not one: two probes drawn adjacent may be 40 kb apart. When the panel has no choice it now prints NOT TO SCALE in bold under the axis, where the caveat survives being cropped into a slide, and messages once on the console naming how to fix it. The bundled Alpha cascade carries an hg38 position for every probe, and dmsa_report() now reads coordinates straight from it, so the default path is always to scale.

Fix

• The minus strand. On a gene that reads right to left the 5’ UTR is the piece with the HIGHER coordinate, and the TSS is at the high end. Getting this backwards puts the promoter at the wrong end of half of all genes; it is pinned by tests on both strands.

dmsa 1.7.2 (2026-08-17)

dmsa_sets_check("alpha") failed the package’s own data

The data sanity gate reported the bundled cascade as not usable on two counts, and both were defects in the check rather than in the data. A validator that cries wolf on the shipped set teaches users to ignore it, so:

• Uniqueness is keyed on probe_id, not cpg. EPIC v2 ships replicate designs for the same site - cg09834343_BC21 and _BC22 are two columns measuring one CpG - so 238 legitimate rows were being counted as duplicates. 115 CpGs in the bundled set carry more than one design; they are now reported as a note, since replicate designs are correlated measurements of one site and that matters for a probe-level family.
• Module evidence is read from the companion audit table. It is shipped beside the cascade rather than inside it, so validating the raw CSV found no evidence_strength at all. "alpha" is now validated as it is actually loaded, and a cascade genuinely carrying no evidence gets a WARN rather than a FAIL - absent evidence changes how a result reads, it does not make the set unusable.

Fixes found by R CMD check --run-donttest

• dmsa_systems("stress|hpa") - the form used in this package’s own documentation - errored with “cascade file not found”. A bare string that is neither "alpha" nor an existing file is now read as a search pattern, which is what the example always meant.
• dmsa_polarity_fetch()’s example moved to \dontrun{} and now writes to tempdir(). It made live HTTP calls during the check: 35 seconds of timeouts on a machine without network, and a stray CSV left in the check directory.
• POLARITY_AUDIT.md, SELECTION_SETS.md and NEWS.md added to .Rbuildignore; they are repository documents, not package files.

494 unit tests.

dmsa 1.7.1 (2026-08-17)

• dmsa_polarity(), dmsa_polarity_review() and dmsa_evidence() accept a dmsa_sets, dmsa_selection or dmsa_frame object, not only a file path. The print banner recommends dmsa_polarity_review(), so the object already in the user’s hand is the obvious argument; passing one used to fail with “cannot coerce class dmsa_selection to a data.frame”.
• The module-label check in .pol_toward_system() uses word boundaries. Without them “sex determination” matched the brake vocabulary and every gonadal module was flagged.

dmsa 1.7.0 (2026-08-17)

Gene-to-system polarity becomes auditable data

w_g is the multiplier that decides which way a system score points, so a single wrong sign does not degrade a result - it inverts it. Until now those signs lived inside a curated CSV with no provenance. They are now a first-class table in which every row states where its sign came from and what would change it.

• dmsa_polarity() loads the table: 1,234 genes across 30 systems, each row carrying w_g, a role, a confidence, a w_g_source and, for 451 rows, a citation. Sources: 112 curated, 121 OmniPath, 46 GtoPdb, 23 TRRUST, 1 UniProt, 228 literature, 676 rule-based, 27 unresolved. Signs: 583 at +1, 291 at -1, 360 held deliberately off-axis at 0.
• dmsa_polarity_review() surfaces the 190 rows a human still has to decide - low-confidence signs, unresolved genes, framing choices and the one database contradiction left standing - and nothing else. which = "disagreement" and which = "unresolved" narrow it.
• dmsa_polarity_check() runs the invariants: an anchor may never lower its own system’s tone, no system may be entirely one-sided, no sign may point at its module’s sub-process rather than the system, and no row may carry a sign without a source. It reports the four known, real exceptions in the Alpha panel rather than passing them silently.
• dmsa_polarity_fetch() drafts signs for a user’s own cascade from GO, OmniPath, TRRUST and SIGNOR. When a resource is unreachable it returns unresolved at w_g = 0, never a guess, and flags every row for review.
• dmsa_polarity_sources() states each resource’s caveat, including the one that matters most: CollecTRI and DoRothEA both assign an activating mode by default when the mode is unknown, so OmniPath’s consensus_stimulation at curation_effort = 1 is a default rather than a finding.

The rule the audit turned on

A gene is signed against the system’s declared tone, not against its immediate molecular partner. FKBP5 restrains GR and GR mediates HPA negative feedback, so FKBP5 is +1 to the HPA system - a brake on a brake. The role vocabulary therefore carries brake-of-brake and feedback-enabler as first-class labels, and separates roles that commit to a direction (driver, brake, …) from roles whose sign is inherited from what they serve (accessory, transducer, capacity, readout).

A database may raise the evidence grade of a sign it agrees with. It may never flip a sign on its own. All 38 database contradictions were adjudicated one at a time against the literature; four proved to be defects at source. w_g for the Project Alpha panel is unchanged from the curated values in every one of the 112 rows the PI set by hand. POLARITY_AUDIT.md records the method, the rejected resources and the escalations.

• The polarity table travels with dmsa_sets() and narrows with dmsa_select(), so a selection reports the coverage of its own genes.
• print() on a set, a selection or a frame names the unsigned and flagged counts instead of hiding them.

dmsa 1.6.0

The selection cascade becomes a first-class object

system > module > gene > probe now ships with the package as a curated selection set, and it is the object dmsa_frame() resolves systems = against. The point is not convenience: because families are declared in this file and never harvested from whatever the analyst happened to load, the family a unit is corrected within is a property of the map rather than of the session. That is the mechanism behind DMSA’s invariance to set selection, and it is now visible.

• dmsa_sets() loads a selection cascade - the bundled, module-audited Project Alpha 2026c set (30 systems, 188 modules, 1,234 genes, 16,823 CpGs) or any CSV in the same shape.
• dmsa_systems() prints the systems with the short name for each, which is what systems = now takes: systems = c("hpa", "oxytocin"). Ids, full names and unique prefixes resolve too; ambiguity errors with the candidates named rather than picking one.
• Everything below the chosen system defaults to full. "hpa" takes all 8 modules, 49 genes and 538 CpGs the set assigns to it. dmsa_select() narrows a level when a narrower question was declared, and says so in its print output, because narrowing changes the family and therefore the toll.
• dmsa_select(columns = "auto", data = dat) keys the cascade to the caller’s matrix through the col_* columns instead of requiring renamed columns.
• dmsa_frame() gains sets = (default "alpha"). The module level now takes its membership from the cascade rather than the legacy modules_alpha.csv, so the family a unit is corrected within is the family the user selected from. sets = NULL restores the legacy path.

Module evidence travels with the result

Every module label in the bundled set was checked against the literature. That check is now part of the output, on the principle that a module-level finding is only as good as the module’s definition.

• dmsa_evidence() returns the evidence tier (High / Moderate), the audit status (supported_as_named, renamed_*, heterogeneous_locked_membership, measurement_defined_module), the citation keys and the evidence note, for a set, a selection or a frame. which = "flagged" reduces it to the modules that need a caveat when reported.
• The counts print with dmsa_sets(), dmsa_select() and dmsa_frame(), and summary.md from dmsa_report() gains a Module evidence table covering the modules the run actually reported on.

Bringing your own panel

• dmsa_sets_template() writes the CSV skeleton plus the full instructions; dmsa_sets_check() validates a candidate file against the five structural rules before it is trusted (one system per module, one module per gene, unique short names, constant module metadata, no duplicate rows) and reports CpGs annotated to more than one gene rather than rejecting them.
• SELECTION_SETS.md documents the schema, the resolution rules and the separation between the selection set, the direction map and the polarity table.

Fixes

• module_id is read as text throughout. Parsed as a number, 24.10 collapses onto 24.1 and two different immune modules become one - a silent, total corruption of the family structure.
• systems = no longer errors when a system id is character rather than numeric.
• data.table moved from an undeclared :: call to Imports.
• Documented the previously undocumented weighting, w_floor, gate, sparse_reach, family_correction and nulls arguments.

dmsa 1.5.0 (2026-08-15)

• ENGINE DEFAULT CONFIRMED: combined. The evidence points two ways and both directions are on the record. On REAL data, pre-registered application to the PTSD and children cohorts showed neither flat nor reliability weighting dominates - reliability gains on concentrated single-gene signals (FKBP5, SGK1) and loses on diffuse system and module signals - so hedging the engine choice keeps every real finding. In SIMULATION, however, flat weighting needs FEWER participants than combined in all fourteen sample-size cells, by 3% at a twelve-gene family (326 vs 337) and by 62% on a three-system family (472 vs 764). The default is combined because the shape of a real signal is not known in advance and the premium for that insurance is roughly 10% more participants in a typical cell; weighting = "flat" is a first-class option for anyone who knows their signal is coherent, and remains the exact reproducibility reference for the published anchors.

• Documentation rendered: man/ now ships 48 Rd files, so ?dmsa_frame and the rest resolve. The package previously carried roxygen comments with no generated documentation.

• inst/scripts/artifact_probe_mask.R - checks EPIC probes carrying a finding against five published artifact lists (cross-reactive, cross-hybridising, SNP at target, INDEL at target, 1000G variant in probe body), with the provenance of every list documented in the header.

• Two tests in test-design.R now qualify an internal call as dmsa:::, so the suite runs outside the package namespace. 343 tests, 0 failures.

• FIXED: the combined engine’s rank-p fusion had a DISCRETENESS FLOOR. Rank p-values are granular at 1/(B+1); under maxT across K units, ties at the extreme floored the family-wise p at about K/(B+1). At K = 42, B = 399 that is .105, so power was exactly 0.000 regardless of effect size (found on an independent K = 42 sweep, 15 Aug 2026; the defect was invisible in our own tests, which never used a family larger than five). Engines are now fused on a CONTINUOUS scale: each lens/engine statistic is standardised by its own permutation null (robust centre/scale) before combination, so no ties and no floor. Verified at K = 42, B = 399.

• NEW: p_unit / p_unit_adj - a unit-level test on ONE joint null across lenses (and engines). The per-lens adjusted p-values correct across UNITS only, so the previously used rule - “name a unit if the minimum of the three lens-adjusted p’s is below .05” - is not family-wise controlled across lenses and inflates the error rate roughly threefold (measured: .06 where .05 was intended, and up to .13 in some grids). p_unit_adj takes the maximum standardised lens statistic per unit and applies maxT over units: one rule, valid across lenses and units. Measured family-wise error .030 (flat) and .040 (combined) at K = 12 against the old rule’s .060. USE p_unit_adj FOR NAMING; the per-lens columns remain for reporting which lens carried a finding.

dmsa 1.4.0 (2026-08-15)

• THREE ENGINES; the COMBINED engine is now the DEFAULT. Pre-registered testing on the PTSD and children cohorts showed neither flat nor reliability dominates on real data: reliability gains on concentrated single-gene signals (FKBP5, SGK1) but loses on diffuse system/module signals (the GR-module PTSD lead, the child HPA-system finding). The combined engine fuses the two per lens by an ACAT (Cauchy) combination of their rank-p’s on ONE shared permutation stream, so the family-wise maxT is an exact JOINT NULL - not a post-hoc ACAT of two adjusted p’s. It keeps every real signal (validated: GR module .0445 flat / .118 reliability / .064 combined; FKBP5 .681 / .189 / .368) for the small toll of hedging the engine choice, in the same spirit as the three-lens triangulation. Both per-permutation regressions are shared across engines (computed once), so combined costs ~2x lens work, not 2x total. weighting = c("combined", "reliability", "flat") throughout; flat remains the exact reproducibility reference. Calibration of the combined omnibus verified (outcome-free weights + pooled-rank fusion keep type I at alpha). Frame print names the active engine. +2 assertions (combined calibration + both-archetype recovery).

dmsa 1.3.0 (2026-08-15)

• TWO ENGINES, reliability weighting now the DEFAULT. Each aligned probe is weighted by its item-rest (centrality) correlation with the rest of its unit - dmsa_relweights(), computed from methylation ALONE so the permutation null stays exact and weighting changes power, not type-I error. In simulation it gains power on units whose probe quality is heterogeneous and ties the flat engine when it is uniform; it therefore ships as the default. weighting = "flat" restores the original equal-weight engine and is the exact reproducibility reference. Threaded through dmsa_frame(), dmsa_report(), dmsa_triangulate(), dmsa_scores() and dmsa_change() via weighting and w_floor (leading-eigenvalue coherence floor, default 1.5). Guards: units with < 3 usable probes, no shared axis, or all-zero clamped weights fall back to flat. dmsa_test() gains a w argument. Frame print reports the active engine. +7 assertions (test-weight.R): calibration, flat reproducibility, power gain on heterogeneous units, guard fallbacks.

dmsa 1.2.0 (2026-08-14)

• dmsa_change() - longitudinal mDMSA on the 2-wave identity: time x S x E == Delta-S x E. Per-unit aligned scores at each wave from the SAME map, Delta-score x exposure through the composite machinery (Freedman-Lane, family blocks), maxT/minP across the level-local family on one shared permutation stream; baseline score as regression-to-the-mean guard; mod2 adds the second moderator (Delta-S x E x mod2). Validated on the Oct-7 cohort (n = 172) before shipping, per the pre-set condition.
• The frame layout moderation/mod/mod2 now carries the three-way end to end (planted-interaction test locked in the suite); in a longitudinal frame Delta IS time, so mod/mod2 are the two remaining factors.
• Frame fixes from the real child-data run: compositional check no longer trips on zero-variance aliased columns; chip_f autofix accepts any chip/chip_T* column; factor covariates and chip are droplevels-ed after complete-case subsetting (empty levels made designs singular).

dmsa 1.1.2 (2026-08-14)

• A completed moderation run with zero survivors now SAYS so: print and summary report units tested, families, B, and the strongest interaction (unit, b, t, raw and adjusted p) - a calibrated null no longer looks like a run that did nothing. Sub-minute runtimes print in seconds.

dmsa 1.1.1 (2026-08-14)

• Fix: the untestable-gene count in frame$untestable and the report Design notes was computed across ALL systems in the data instead of the chosen systems subset (spotted by comparing the Mac and cloud anchor runs - the statistics were never affected, only the coverage sentence).

dmsa 1.1.0 (2026-08-14)

The two-call user interface.

• dmsa_frame() - declare everything once: data, levels (system / module / gene / probe), outcomes, covariates (“contract” default), random-effect blocks, moderation, map, styling. Runs the TEST DRIVE automatically (autofix = TRUE): numeric coercion with per-column loss report, QR rank audit dropping aliased columns by name (compositional groups flagged), block audit with equal-size permutation strata, confounded-block flags, hard error only when fewer than ~2^10 distinct permutations exist, binary outcome under gaussian autofixed to logistic, B = 49 pilot with a printed ETA. Every action lands in frame$corrections, printed by print(frame) and repeated in the report’s Design notes.
• dmsa_report(frame) - runs dmsa_triangulate() at each requested level with correction ONLY inside level-local families (systems named; modules of the system; genes of the system; probes of surviving genes), writes figures/ tables/ summary.md under frame$outdir, and returns the paths.
• Moderation layout: moderation = FALSE (default), mod = "", mod2 = "". moderation = TRUE + mod tests frame x mod through the composite lens (the one lens that carries to products), maxT across units on a shared permutation stream; mod2 declares frame x mod x mod2 - the 3-way engine ships in 1.2.0 after tonight’s longitudinal test drive, and 1.1.0 raises a clear error if mod2 is set.
• frame_role = "predictor" | "outcome" - methylation as predictor (default) or as the outcome of the named columns; the composite lens flips the score to the left-hand side, narration switches to expression-tone language.
• cpg_map = "confidence" | "full" - both alignments are always built; any gene or system whose pooled direction flips between maps is analysed under the chosen map but warned about, stored in frame$map_conflicts, and repeated in the report.
• Brain-bridge post-hoc, always on: surviving units’ probes go through cpgdirection::cpg_brain_bridge(); hit rule fixed as bridge_usable | has_t2_direction | !is.na(bridge_grade); ensemble_pctile is context. Errors never kill a report.
• dmsa_triangulate() now returns NA (not a p = 1 placeholder) for units with zero usable direction-called probes; dmsa_frame() tracks them in frame$untestable and the report counts them per family.
• dmsa_model(nulls = TRUE) returns its permutation null (null_t) for family maxT across separately fitted models.
• The former figure helper dmsa_report() is now dmsa_report_panels().
• gt moved into Suggests: tables use gt when installed, otherwise a self-contained HTML fallback (CSVs are always written).

Anchor (real Alpha data, systems 1 + 21, B = 1999, seed 1, cpg_map = “full”): AVP coherence adj .0120, PLAGL1 composite adj .0080 - identical to the locked triangulation run. New from the always-on bridge: two PLAGL1 probes carry a formal brain bridge (cg08263357, ensemble percentile 94; cg11532302).