drmSEM

Keep drmSEM CRAN-clean — R CMD check 0 errors/0 warnings, examples that need drmTMB wrapped in \dontrun, Remotes for the GitHub engine, no compiled code in drmSEM itself, DESCRIPTION hygiene, ASCII, reasonable file sizes, reproducible RNG. Use before release or when touching DESCRIPTION/examples/CI.

Reader-facing docs for drmSEM — README, the vignette, and pkgdown reference sync. Pair equations + R syntax + interpretation, show component-labelled output and the distribution-mediated decomposition, and tell users what to try when a path/family is unsupported. Use when writing user-facing prose.

THE core skill. Enforces component-labelled paths — never describe or compute a non-mu path as a mean effect. Use for ANY prose, docs, message, or code that states what a drmSEM path MEANS.

How drmSEM talks to drmTMB — ONLY through R/extractors.R. The drmTMB return-shape facts, the dpar:term coefficient convention, and the rule to confirm parameterizations against a live fit before trusting samplers.

Build basis sets and run any-component d-separation / Fisher's C correctly — adjacency = direct parent on ANY component, causal ordering, conditioning on parents, LRT df bookkeeping, honest framing as drmSEM's definition.

R package craft for drmSEM — roxygen2 on every export, S3 conventions, the document/test/check dev loop, NAMESPACE sync, cli messaging, tests shipped with implementation, small focused commits.

The simulation-based effect engine — do()-style topological propagation, mean vs distribution mediation, direct/indirect/total decomposition, Monte-Carlo coefficient uncertainty. Use whenever computing or describing an effect estimand. Coefficient-product mediation is banned.

Testing discipline for drmSEM — pure-logic kernels run without drmTMB, integration tests gated by skip_if_not_installed, every effect/estimand/d-sep rule needs a known-DGP recovery test, evidence tiers recorded in the validation ledger. Use when adding tests or any new estimand/rule.