Use generic 'ArviZ object' phrasing instead of 'DataTree' in user-facing docs (#25)

PR #21 added support for arviz <1.0 (InferenceData) alongside >=1.0 (DataTree).
This change removes DataTree-specific language from all user-facing doc files so
the phrasing works regardless of arviz version.

Author: drbenvincent

README.md

@@ -4,7 +4,7 @@
 
 > A tidybayes-inspired data layer for declarative Bayesian visualisation in Python
 
-`tidydraws` turns MCMC output (ArviZ `DataTree`) into tidy Polars frames that are ready to plot — one `.to_pandas()` away from any ggplot-like backend. It does no plotting itself. Three functions, three spaces:
+`tidydraws` turns MCMC output (ArviZ) into tidy Polars frames that are ready to plot — one `.to_pandas()` away from any ggplot-like backend. It does no plotting itself. Three functions, three spaces:
 
 | Function | Space | Plot archetype |
 | --- | --- | --- |

docs/examples/01-parameter_draws.qmd

@@ -4,7 +4,7 @@ description: "Fit a PyMC model, extract posterior draws with parameter_draws(),
 status: stable
 ---
 
-`parameter_draws()` is the entry point for parameter-space plots: it pulls posterior draws out of an ArviZ `DataTree` into a tidy Polars `DataFrame` — one row per `chain × draw × coordinate`, each variable a column. This example starts with simulated observed data, fits a real PyMC model, and then uses the resulting posterior draws for densities, intervals, contrasts, and cross-parameter plots.
+`parameter_draws()` is the entry point for parameter-space plots: it pulls posterior draws out of an ArviZ object into a tidy Polars `DataFrame` — one row per `chain × draw × coordinate`, each variable a column. This example starts with simulated observed data, fits a real PyMC model, and then uses the resulting posterior draws for densities, intervals, contrasts, and cross-parameter plots.
 
 ## Auto-detected dimensions
 

docs/examples/02-compare_draws.qmd

@@ -4,7 +4,7 @@ description: "Fit a PyMC model, compare prior and posterior draws with compare_d
 status: stable
 ---
 
-`compare_draws()` is the prior/posterior comparison helper: it calls `parameter_draws()` once per selected group in the ArviZ `DataTree`, tags each result with a `source` column, and stacks them into a single `DataFrame`. By default it compares `posterior` against `prior`, which is the workflow most users need after fitting a PyMC model.
+`compare_draws()` is the prior/posterior comparison helper: it calls `parameter_draws()` once per selected group in the ArviZ object, tags each result with a `source` column, and stacks them into a single `DataFrame`. By default it compares `posterior` against `prior`, which is the workflow most users need after fitting a PyMC model.
 
 ## Full PyMC workflow
 
@@ -250,4 +250,4 @@ ci = td.point_interval(compare, "beta", group_by=["groups", "source"], probs=(0.
 
 :::
 
-`group_name` sets the source column's name, and `groups` can compare other `DataTree` groups when needed. Next, join posterior expectations to covariates with [`prediction_draws()`](prediction_draws.qmd).
+`group_name` sets the source column's name, and `groups` can compare other ArviZ groups when needed. Next, join posterior expectations to covariates with [`prediction_draws()`](prediction_draws.qmd).

docs/examples/03-prediction_draws.qmd

@@ -6,7 +6,7 @@ status: stable
 
 `prediction_draws()` joins posterior prediction draws such as `mu` to covariates, producing one tidy row per `chain × draw × observation`. Parameters are deliberately **excluded** — prediction space stays separate from parameter space, so coefficients never get duplicated across observations.
 
-Pass a `DataFrame` when you want explicit control over the covariates used for plotting. With `newdata=None`, `prediction_draws()` reads covariates from a constant-data group in the `DataTree` and fails loudly if that group is missing.
+Pass a `DataFrame` when you want explicit control over the covariates used for plotting. With `newdata=None`, `prediction_draws()` reads covariates from a constant-data group in the ArviZ object and fails loudly if that group is missing.
 
 ## Full PyMC workflow
 

docs/examples/04-showcase.qmd

@@ -4,7 +4,7 @@ description: "Stress-test tidydraws across model archetypes — one canonical pl
 status: experimental
 ---
 
-`tidydraws` is tested against synthetic DataTrees. This page stress-tests the full pipeline — PyMC model → `DataTree` → `parameter_draws` / `prediction_draws` / `compare_draws` → plot — across eight model archetypes that each exercise a different code path.  If this page builds, the data layer survived that shape.  If it breaks, you know exactly which archetype is the culprit.
+`tidydraws` is tested against synthetic ArviZ objects. This page stress-tests the full pipeline — PyMC model → ArviZ object → `parameter_draws` / `prediction_draws` / `compare_draws` → plot — across eight model archetypes that each exercise a different code path.  If this page builds, the data layer survived that shape.  If it breaks, you know exactly which archetype is the culprit.
 
 ```{python}
 # | code-fold: true

great-docs.yml

@@ -32,7 +32,7 @@ sections:
 # API Reference ---------------------------------------------------------------
 reference:
   - title: Functions
-    desc: Extract, join, and summarise tidy draws from ArviZ DataTrees.
+    desc: Extract, join, and summarise tidy draws from ArviZ objects.
     contents:
       - parameter_draws
       - prediction_draws

index.qmd

@@ -6,7 +6,7 @@ title: "tidydraws"
 ![](docs/assets/logo.jpg)
 A tidybayes-inspired data layer for declarative Bayesian visualisation in Python.
 
-`tidydraws` turns MCMC output (ArviZ `DataTree`) into tidy Polars frames that are ready to plot — one `.to_pandas()` away from any ggplot-like backend. It does no plotting itself. Four functions, four roles:
+`tidydraws` turns MCMC output (ArviZ) into tidy Polars frames that are ready to plot — one `.to_pandas()` away from any ggplot-like backend. It does no plotting itself. Four functions, four roles:
 
 | Function | Role | Plot archetype |
 | --- | --- | --- |