Fix llms-full.txt accuracy and dynamic canonical URLs

Address AI review P1/P2 findings: remove fabricated covariates param
from ContinuousDiD.fit(), fix aggregate enum values to match source
(None/dose/eventstudy), document TROP treatment as absorbing-state
indicator, fix lambda_nn_grid default, give ContinuousDiDResults its
own results section with correct ATT/ACRT schema, and use RTD's
READTHEDOCS_CANONICAL_URL for version-aware html_baseurl/ogp_site_url.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: igerber

docs/conf.py

@@ -65,7 +65,12 @@
 html_theme = "pydata_sphinx_theme"
 html_static_path = ["_static"]
 html_title = "diff-diff: Difference-in-Differences Causal Inference for Python"
-html_baseurl = "https://diff-diff.readthedocs.io/en/stable/"
+# Use RTD's canonical URL when available; fall back to stable for local builds.
+_canonical_url = os.environ.get(
+    "READTHEDOCS_CANONICAL_URL",
+    "https://diff-diff.readthedocs.io/en/stable/",
+)
+html_baseurl = _canonical_url
 html_extra_path = ["llms.txt", "llms-full.txt"]
 sitemap_url_scheme = "{link}"
 
@@ -95,7 +100,7 @@
 }
 
 # -- Options for sphinxext-opengraph -----------------------------------------
-ogp_site_url = "https://diff-diff.readthedocs.io/en/stable/"
+ogp_site_url = _canonical_url
 ogp_site_name = "diff-diff"
 ogp_description_length = 200
 ogp_type = "website"

docs/llms-full.txt

@@ -465,8 +465,7 @@ cdid.fit(
     time: str,
     first_treat: str,
     dose: str,                     # Column with continuous treatment dose
-    covariates: list[str] = None,
-    aggregate: str = None,         # None, "simple", "dose", "event_study"
+    aggregate: str = None,         # None, "dose", "eventstudy"
 ) -> ContinuousDiDResults
 ```
 
@@ -580,7 +579,7 @@ TROP(
     method: str = "twostep",                  # "twostep" or "global" (or deprecated "joint")
     lambda_time_grid: list[float] = None,     # Time weight decay grid [0, 0.1, 0.5, 1, 2, 5]
     lambda_unit_grid: list[float] = None,     # Unit weight decay grid [0, 0.1, 0.5, 1, 2, 5]
-    lambda_nn_grid: list[float] = None,       # Nuclear norm grid [0, 0.01, 0.1, 1]
+    lambda_nn_grid: list[float] = None,       # Nuclear norm grid [0, 0.01, 0.1, 1, 10]
     max_iter: int = 100,
     tol: float = 1e-6,
     alpha: float = 0.05,
@@ -595,7 +594,7 @@ TROP(
 trop.fit(
     data: pd.DataFrame,
     outcome: str,
-    treatment: str,                # Treatment indicator (0/1, can vary over time)
+    treatment: str,                # Absorbing-state treatment indicator (0/1). Must be 0 for all pre-treatment periods and 1 for treatment and post-treatment periods.
     unit: str,
     time: str,
 ) -> TROPResults
@@ -865,7 +864,7 @@ Individual 2x2 DiD comparison (used in BaconDecompositionResults).
 
 ### Common Results Pattern for Staggered Estimators
 
-ImputationDiDResults, TwoStageDiDResults, StackedDiDResults, EfficientDiDResults, and ContinuousDiDResults all share a similar structure:
+ImputationDiDResults, TwoStageDiDResults, StackedDiDResults, and EfficientDiDResults share a similar structure:
 
 | Attribute | Type | Description |
 |-----------|------|-------------|
@@ -886,6 +885,44 @@ Each event study effect dict contains: `effect`, `se`, `t_stat`, `p_value`, `con
 
 **Methods:** `summary()`, `print_summary()`, `to_dataframe()`
 
+### ContinuousDiDResults
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `dose_response_att` | `DoseResponseCurve` | Dose-response curve for ATT |
+| `dose_response_acrt` | `DoseResponseCurve` | Dose-response curve for ACRT |
+| `overall_att` | `float` | Overall ATT |
+| `overall_att_se` | `float` | SE of overall ATT |
+| `overall_att_t_stat` | `float` | T-statistic for ATT |
+| `overall_att_p_value` | `float` | P-value for ATT |
+| `overall_att_conf_int` | `tuple[float, float]` | CI for ATT |
+| `overall_acrt` | `float` | Overall ACRT |
+| `overall_acrt_se` | `float` | SE of overall ACRT |
+| `overall_acrt_t_stat` | `float` | T-statistic for ACRT |
+| `overall_acrt_p_value` | `float` | P-value for ACRT |
+| `overall_acrt_conf_int` | `tuple[float, float]` | CI for ACRT |
+| `group_time_effects` | `dict[tuple, dict]` | Group-time level effects |
+| `dose_grid` | `np.ndarray` | Evaluation grid for dose-response |
+| `groups` | `list` | Treatment cohorts |
+| `time_periods` | `list` | All time periods |
+| `n_obs` | `int` | Number of observations |
+| `n_treated_units` | `int` | Treated units |
+| `n_control_units` | `int` | Control units |
+| `event_study_effects` | `dict[int, dict] or None` | Event study effects (if `aggregate="eventstudy"`) |
+
+**DoseResponseCurve** sub-dataclass:
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `dose_grid` | `np.ndarray` | Dose values |
+| `effects` | `np.ndarray` | Estimated effects at each dose |
+| `se` | `np.ndarray` | Standard errors |
+| `conf_int_lower` | `np.ndarray` | Lower CI bound |
+| `conf_int_upper` | `np.ndarray` | Upper CI bound |
+| `target` | `str` | `"att"` or `"acrt"` |
+
+**Methods:** `summary()`, `print_summary()`, `to_dataframe()`
+
 ### TROPResults
 
 | Attribute | Type | Description |