v0.2.0

Added

• Ported read_fusion (gsem::io::fusion_reader + CLI gsem read-fusion). Reads raw FUSION TWAS .dat association files and converts
  each trait's TWAS.Z into a standardized gene-expression effect/SE (binary
  traits via the liability conversion effect/√(effect²·HSQ + π²/3),
  continuous via Z/√(N·HSQ)), supports the permutation-Z path, then
  inner-joins traits on Gene+Panel into the merged TWAS format that
  twas_reader / multiGene / userGWAS(TWAS=TRUE) consume. This is the
  on-ramp from raw FUSION output into the Rust TWAS path (previously only
  pre-merged files could be read). Validated against live R read_fusion.

• Ported subSV (gsem_matrix::vech::subset_sv). Subsets vech(S) and
  the V sampling-covariance block by a set of 1-based vech positions, for
  both the with-diagonal (TYPE="S"/"S_Stand") and off-diagonal
  (TYPE="R") numbering. Validated against R to 1e-12. (R's matrix-input
  path has an undefined-RMATRIX bug; the port matches the bug-free
  LDSC_OBJECT path.)

• Ported summaryGLSbands numeric core
  (gsem::stats::gls::summary_gls_bands). GLS fit plus the confidence-band
  data — predictor grid, fitted line, and ±BAND_SIZE·SE envelope at
  INTERVALS points, with INTERCEPT/QUAD/CONTROLVARS support.
  Validated against R to 1e-9. The ggplot rendering is not ported.

• Per-option R-equivalence coverage for the drop-in surface. New
  real-package fixtures and tests for previously-untested option branches:
  userGWAS estimation="ML" / GC={conserv,none} / Q_SNP / std.lv;
  ldsc stand=TRUE (S_Stand/V_Stand) / select="ODD" / chisq.max /
  liability-scale; sumstats ambig=TRUE (full numeric parity); a
  non-degenerate misspecified-model CFI/chisq fixture; the paLDSC diag
  branch; and bindings parity suites (R testthat + Python pytest).

• Coverage instrumentation in CI. A cargo-llvm-cov coverage job with a
  ratcheting floor, Codecov upload + README badge, and an advisory R-side
  covr job.

• R documentation site (pkgdown). A published docs site for the gsemr
  binding (reference index + Compatibility/Architecture articles generated
  from the repo-root docs), deployed to GitHub Pages, with a navbar link to
  the upstream GenomicSEM project.

Fixed

• std.lv left the factor scale unidentified. parse_model(std_lv=true)
  freed the auto-added latent variance instead of fixing it to 1 (lavaan's
  std.lv=TRUE convention: auto.fix.first=FALSE + latent variances fixed
  to 1). Any std.lv model — userGWAS / usermodel / rgmodel(std_lv=) —
  estimated an unidentified factor scale and was ~12% off R. Now the
  auto-add fixes the latent variance under std_lv. (crates/gsem-sem/src/syntax.rs)

• Q_SNP heterogeneity statistic + LDSC intercept floor. compute_q_snp
  computed the wrong quadratic form, and the LDSC intercept diagonal was not
  floored at 1.0 before building the per-SNP V (R's userGWAS.R:156).
  Both fixed; Q_SNP now matches R to ~1e-9.
  (crates/gsem/src/gwas/{q_snp,gc_correction,user_gwas}.rs)

---

The following entries accumulated on master after 0.1.3 and ship in 0.2.0:

Added

• R-equivalence coverage for multiSNP, multiGene, simLDSC, and
  hdl. New live-R fixtures and tests validate the four previously
  untested functions, closing the last function-coverage gaps. multiSNP
  and multiGene reproduce R's augmented S_Full/V_Full matrices to
  1e-12/1e-14; simLDSC's deterministic per-SNP Z covariance matches R's
  exact varZ/covZ algebra to 1e-9; hdl reproduces R's genetic-
  covariance matrix S to optimiser-level precision on a synthetic LD
  panel built in R's exact .rda/.bim format.

Changed

• hdl evaluated in the eigenspace (match R). gsem's HDL likelihood
  was fed raw LD scores as lam and the raw per-SNP bhat, whereas R
  GenomicSEM/HDL evaluates the likelihood in the LD-block eigenspace:
  lam = eigenvalues of the block correlation matrix and
  bstar = Vᵀ·bhat (projection onto eigenvectors). LdPiece now carries
  the per-piece eigenvalues/eigenvectors (read from the .rda panel, or
  the new chr*.eigen.tsv text file emitted by convert_hdl_panels), the
  per-trait reference N uses R's median(N), the likelihood floor matches
  R's exp(-18), and the per-piece optimiser is a projected-gradient
  method mirroring R's optim(L-BFGS-B). (crates/gsem-ldsc/src/hdl.rs)

Fixed

• simLDSC phenotypic-overlap term. The off-diagonal environmental
  contribution to the per-SNP Z covariance was
  rPheno·n_overlap·√(NᵢNⱼ)/n_snps, carrying a spurious √(NᵢNⱼ)/n_snps
  factor. R GenomicSEM uses rPheno_ij · N_ij/√(NᵢNⱼ) (= rPheno·n_overlap
  for scalar sample overlap). Fixed and exposed as the deterministic
  per_snp_z_cov, validated against R's construction.
  (crates/gsem/src/stats/simulation.rs)

• multiSNP/multiGene V_Full construction. run_multi_snp built
  V_Full as a diagonal approximation that ignored the LDSC intercept
  matrix. The new build_multi_snp_sv reproduces R's full sampling
  covariance — SNP-trait variances (SE·I_diag·varSNP)², cross-trait
  within-SNP, cross-SNP within-trait, and cross-SNP cross-trait blocks,
  plus the trait-trait V_LD block — matching R bit-for-bit.
  (crates/gsem/src/gwas/multi_snp.rs)

Documented R bugs (gsem implements the correct behavior)

• multiSNP/multiGene constant cross-SNP cross-trait LD. R weights
  every cross-SNP cross-trait sampling-covariance cell by a single
  constant LD value (LD2[(f²−f)/2], the last lower-triangle entry)
  rather than the actual SNP-pair LD. gsem uses the correct per-pair
  LD[a,b]; the two coincide when the off-diagonal LD is constant (as in
  the equivalence fixtures). A unit test locks gsem's corrected path.

• multiGene aborts for k ≥ 2 traits. R's multiGene assigns into
  V_SNP[y,x] — a variable that does not exist in multiGene (a typo for
  V_Gene) — so the function errors with object 'V_SNP' not found
  whenever there is more than one trait. gsem implements the corrected
  algorithm (identical to multiSNP with gene heritabilities as the
  "variances"); the reference is generated from a minimally-patched
  multiGene.

• hdl intercept weak identification. The per-piece HDL intercept
  enters the likelihood only as int·lam/N and is weakly identified on
  small panels; R's optim drifts on some LD blocks while gsem's gradient
  method recovers the construction-true intercept. The genetic-covariance
  matrix S (HDL's primary output) is robustly identified and matches R.

• sumstats beta standardization. gsem's sumstats wrote the raw
  input betas/SEs instead of standardizing them like R GenomicSEM. Since
  userGWAS/commonfactorGWAS build cov(SNP, trait) = varSNP · beta,
  this propagated a sqrt(varSNP) scale error into per-SNP GWAS output.
  Now implements all of R's modes — OLS (beta = Z/√(N·varSNP)),
  linprob, se.logit, the default logistic "none" transform, and
  betas pass-through — reading the P and N columns and deriving Z from
  P. Validated formula-for-formula against R for every mode.
  (crates/gsem/src/sumstats.rs)

• sumstats ambiguous-SNP default. R GenomicSEM removes
  strand-ambiguous SNPs (A/T, C/G) only when ambig=TRUE; its default
  keeps them. The R and Python bindings (and the CLI) mapped
  keep_ambig = ambig, so their default dropped ~⅓ of SNPs where R keeps
  them. Fixed to keep_ambig = !ambig across the R binding, Python
  binding, and CLI (which gains an --ambig flag), matching R's default.

• rgmodel V_R dimension. rgmodel returned V_R as the full
  kstar × kstar sampling covariance of vech(R) (including the fixed
  diagonal-1 correlations, which have ~zero variance). R GenomicSEM returns
  V_R for the off-diagonal correlations only (k(k−1)/2 square). Fixed to
  extract the off-diagonal vech block, matching R's shape and ordering (so
  the bindings now return R's shape too). (crates/gsem-sem/src/rgmodel.rs)

• enrich model-based functional enrichment. gsem's enrich
  (enrichment_test) computed the classic LDSC heritability-enrichment ratio
  (prop_h²/prop_SNPs, the original Finucane statistic), not R GenomicSEM's
  model-based enrich. R fits a SEM to a baseline annotation, fixes the
  regressions/loadings (per fix) to their baseline estimates, re-fits each
  annotation freeing the remaining parameters, and reports per-parameter
  enrichment = (est_annot/est_baseline)/Prop with SE and a 1-sided p-value.
  Added gsem_sem::enrich_model::model_enrichment (+ a shared gsem_sem::fit
  helper) implementing this with the regressions/covariances/variances fix
  modes, wired through the R binding (enrich(s_covstruc, model, params, fix))
  and the Python binding (model_enrichment). Validated against live R
  GenomicSEM on a factor-variance covstruc. gsem's classic ratio is retained
  as enrichment_test. (crates/gsem-sem/src/enrich_model.rs)

• s_ldsc overlap-weighted partitioned heritability. Stratified LDSC
  returned per-annotation S as the raw coefficient contribution tau · M
  (R's S_Tau), missing R's overlap weighting. R computes each category's
  partitioned (co)heritability as S = overlap · (tau · M), where
  overlap[f,a] = M(f,a)/M_a and M(f,a) is the number of SNPs in both
  annotations f and a (from the .annot.gz membership + .frq MAF
  filter). These agree only for disjoint annotations and diverge for the
  realistic overlapping baselineLD model. Added a .annot.gz/.frq
  cross-product reader and applied the overlap transform to both S and the
  jackknife-based V; the result now exposes s_annot/v_annot (R's
  S/V) and s_tau/v_tau (R's S_Tau/V_Tau). Threaded through the R
  binding, Python binding, and CLI (new --frq flag). Validated against R
  on synt...

v0.1.3

Critical fix for cross-trait LDSC

Fixed

• LDSC cross-trait allele alignment. Z-scores were not flipped when A1 differed between traits, producing wrong-sign genetic covariance and ~10x inflated cross-trait intercepts. Diagonal (heritability) estimates were unaffected. All users running multi-trait LDSC should upgrade.
• LDSC per-trait chi² filtering now matches R GenomicSEM's pre-filter-then-merge behavior. Cross-trait SNP counts now agree exactly with R.

Full Changelog: v0.1.2...v0.1.3

v0.1.2

What's Changed

Fixed

• Equality constraints (a*V1 + a*V2 syntax) now correctly share a single free parameter. Previously each labelled term got its own independent parameter, silently ignoring the constraint.
• Labelled first-indicator loadings (e.g. F1 =~ a*V1 + a*V2) are now free, not fixed to 1. Matches lavaan semantics.
• Observed variable detection for covariance-only models (RL ~~ MDD) no longer fails with "0 observed variables".
• L-BFGS convergence flag: when the line search exhausts all step sizes, the optimizer is at a stationary point — now correctly reports converged = true.

Added

• Heywood case warnings: negative variance estimates are detected after every SEM fit and logged at WARN level via the log crate. Surfaces in R, Python, and CLI.
• New tests for equality constraints (shared params, value propagation, implied covariance, Jacobian) and negative variance detection.

Full Changelog: v0.1.1...v0.1.2

v0.1.1

Second release of GenomicSEM-rs. Patch release on top of v0.1.0 with
pre-built R binaries, Windows R support, the CHANGELOG.md file, and
a handful of R CMD check fixes.

Full diff: v0.1.0...v0.1.1

Added

• Pre-built R binary packages for Linux, macOS, and Windows attached
  to every GitHub release. Users no longer need a Rust toolchain to
  install gsemr — pick the platform-native file from the release page
  and run install.packages(<url>, repos = NULL). Source tarball +
  remotes::install_github remain as the "from source" fallback for
  unmatched R versions and dev builds.
• R package now ships with full Windows build support: configure.win,
  src/Makevars.win, and -Wl,--export-all-symbols in the MinGW link
  line so R .Call("wrap__*_rust", ...) resolves at runtime on
  Windows. R CMD check is green on ubuntu / macos / windows.
• workflow_dispatch trigger on publish.yml with a release_tag
  input, so the R-only job path can be manually re-run against an
  existing release (to ship a late R binary or fix without burning a
  crates.io / PyPI version slot).
• readme = "README.md" and [project.urls] in
  bindings/python/pyproject.toml so the PyPI project page renders a
  description with links to the repo, issue tracker, and architecture
  docs. (The 0.1.0 wheels shipped before this landed — the PyPI 0.1.0
  page will continue to show "no project description" until 0.1.1 is
  uploaded.)

Changed

• bindings/python/README.md: absolute GitHub URLs instead of
  relative ../../API_COMPAT.md / ../../ARCHITECTURE.md links
  (which don't render on PyPI); function list expanded from 10 to all
  17 exports and grouped as Core pipeline vs Advanced.
• Root README.md: R install section restructured — binary
  packages as the primary path, source install demoted to a "From
  source" subsection. New Rust MSRV (1.88+) badge.
• R package internal cleanup:
  • Explicit importFrom(stats, pnorm, setNames) /
    importFrom(utils, read.table, write.table) in NAMESPACE,
    clearing the "no visible global function definition" notes.
  • @param out added to sumstats.R + regenerated sumstats.Rd
    (fixes an "Undocumented arguments in Rd file" WARNING).
  • {chr} in ldsc.R's @param ld description escaped to
    \code{<chr>.l2.ldscore.gz} (fixes "Lost braces" NOTE).
  • configure / configure.win print rustc --version / cargo --version before the build (fixes "No rustc version reported"
    WARNING).
  • configure / configure.win now rm -rf src/rust/target after
    copying out libgsemr.a, so R CMD check doesn't scan a duplicate
    rust/target/release/libgsemr.a and double-report the Rust
    stdlib's _exit / abort / exit symbols.

Removed

• PyLdscResult.to_json() and gsem.LdscResult.from_json(s) — the
  json: String field and the conversions::json_to_ldsc helper that
  backed them. These were dead compat from the pre-NumPy era; every hot
  path has been reading s / v / i_mat through NumPy getters for a
  while. This is the only user-visible break in 0.1.1. Callers who
  were serializing LDSC results to disk should use pickle or pass the
  result object directly into downstream functions (all 17 exported
  functions accept it).

Fixed

• read_sumstats aborted on NA tokens, so .sumstats.gz files that
  worked in GenomicSEM::ldsc failed in gsemr::ldsc with a cryptic
  gsemr::ldsc error: invalid N long before sample.prev /
  population.prev were even evaluated. GenomicSEM tolerates these
  files because its reader calls na.omit(read_delim(...)); gsemr now
  matches that behaviour — rows whose N or Z field is an NA token
  (empty, ., NA, NaN, N/A, NULL; case-insensitive) are
  silently dropped, with a one-line INFO log naming the file and the
  dropped-row count. Genuine parse failures now report file path, line
  number, and the offending value, and load_trait_data wraps the
  error with the failing file path so multi-trait runs tell you which
  input went bad. (crates/gsem/src/io/gwas_reader.rs)
• publish.yml skip cascade: check-r-package and
  upload-r-to-release no longer inherit the implicit if: success()
  gate, which was evaluating to false on workflow_dispatch runs
  (because publish-crates is skipped there). They now use
  if: always() && needs.<upstream>.result == 'success' and proceed
  as long as their direct needs are green.
• Versioned workspace deps: root Cargo.toml internal deps now
  carry version = "0.1.0" alongside path, required by
  cargo publish for inter-crate references.
• R binding Cargo patch: bindings/r/src/rust/Cargo.toml declares
  internal crates at crates.io versions and uses [patch.crates-io]
  to redirect to the in-repo sources when available. configure
  strips the patch block when the local crates aren't reachable
  (tarball install), so cargo falls back to the published crates
  cleanly.

v0.1.0

First tagged release of GenomicSEM-rs.

A Rust rewrite of R GenomicSEM, shipped as a 6-crate workspace with
R (gsemr) and Python (genomicsem) bindings plus a standalone CLI
(gsem).

What's in this release

Core engines (crates.io)

• gsem-matrix — matrix utilities (nearest PD, half-vec, PSD smoothing)
• gsem-ldsc — LD Score Regression with block jackknife
• gsem-sem — SEM engine (DWLS/ML, lavaan syntax, sandwich SEs)
• gsem — pipeline + CLI binary (munge, ldsc, s_ldsc, hdl, sumstats,
  commonfactor, usermodel, userGWAS, commonfactorGWAS, rgmodel,
  write.model, paLDSC, enrich, multiSNP, simLDSC, summaryGLS)

Bindings

• gsemr — R package (source tarball attached below)
• genomicsem — Python package (PyPI wheels for Linux/macOS/Windows)

Highlights

• End-to-end drop-in compatibility with R GenomicSEM's public API on
  all 18 user-facing functions, with the same argument shapes, output
  layouts, and file formats.
• Significant wall-clock speedups on the PGC benchmark (see README
  comparison table).
• Every user-facing function on every surface (R, Python, CLI) ships
  with a worked Examples block in its help output.

See README.md for the installation and usage walk-through.