Generalise interpretive rules for multi-guideline support (#268) (#283)

* Generalise interpretive rules for multi-guideline support (#268)

- Rename data-raw/eucast_rules.tsv → interpretive_rules.tsv; add rule.provider
  column (value: "EUCAST") to distinguish future CLSI rows
- Rename EUCAST_RULES_DF → INTERPRETIVE_RULES_DF in _pre_commit_checks.R;
  filter by rule.provider == guideline when applying rules in interpretive_rules()
- Rename custom_eucast_rules() → custom_interpretive_rules() with new S3 class
  "custom_interpretive_rules"; old function becomes a deprecated wrapper in
  zz_deprecated.R; backward-compat S3 dispatch shims added for old class
- Remove stop_if(guideline == "CLSI", ...) so clsi_rules() no longer errors
- Add .onLoad shim in zzz.R to create INTERPRETIVE_RULES_DF from EUCAST_RULES_DF
  for transitional compatibility until sysdata.rda is regenerated

https://claude.ai/code/session_01D46BTsfJSPo3HnLWp3PRkP

* Fix namespace load failure: remove assignInNamespace from .onLoad (#268)

assignInNamespace cannot add NEW bindings to a locked package namespace
(R locks namespace bindings before .onLoad runs). Replace the .onLoad
shim with a runtime fallback inside interpretive_rules(): if
INTERPRETIVE_RULES_DF is absent (pre-regeneration sysdata.rda), derive
it from EUCAST_RULES_DF by adding the rule.provider column. This also
fixes the screening_abx line to reuse the already-resolved
interpretive_rules_df_total instead of a bare INTERPRETIVE_RULES_DF
reference.

https://claude.ai/code/session_01D46BTsfJSPo3HnLWp3PRkP

* fixes

* fixes

---------

Co-authored-by: Claude <noreply@anthropic.com>

man/g.test.Rd

@@ -45,8 +45,9 @@ A list with class \code{"htest"} containing the following
   \item{residuals}{the Pearson residuals,
     \code{(observed - expected) / sqrt(expected)}.}
   \item{stdres}{standardized residuals,
-    \code{(observed - expected) / sqrt(V)}, where \code{V} is the residual cell variance (Agresti, 2007,
-    section 2.4.5 for the case where \code{x} is a matrix, \code{n * p * (1 - p)} otherwise).}
+    \code{(observed - expected) / sqrt(V)}, where \code{V} is the
+    residual cell variance (Agresti, 2007, section 2.4.5
+    for the case where \code{x} is a matrix, \code{n * p * (1 - p)} otherwise).}
 }
 \description{
 \code{\link[=g.test]{g.test()}} performs chi-squared contingency table tests and goodness-of-fit tests, just like \code{\link[=chisq.test]{chisq.test()}} but is more reliable (1). A \emph{G}-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a \strong{\emph{G}-test of goodness-of-fit}), or to see whether the proportions of one variable are different for different values of the other variable (called a \strong{\emph{G}-test of independence}).