docs: replace pip install with uv add in README

Author: burningcost

README.md

@@ -16,7 +16,7 @@ GLMs have been the industry standard for decades. They're interpretable, well-un
 ## Quick Start
 
 ```bash
-pip install "insurance-gam[ebm]"
+uv add "insurance-gam[ebm]"
 ```
 
 > 💬 Questions or feedback? Start a [Discussion](https://github.com/burning-cost/insurance-gam/discussions). Found it useful? A ⭐ helps others find it.
@@ -64,7 +64,7 @@ print(rt.summary())
 
 Wraps [interpretML's](https://github.com/interpretml/interpret) `ExplainableBoostingRegressor` with insurance-specific tooling: exposure-aware fit/predict, relativity table extraction, post-fit monotonicity enforcement, and GLM comparison tools. If you want the interpretability of a GLM with the predictive power of a gradient booster, start here.
 
-Requires the `[ebm]` extra: `pip install "insurance-gam[ebm]"`
+Requires the `[ebm]` extra: `uv add "insurance-gam[ebm]"`
 
 ```python
 import numpy as np
@@ -107,7 +107,7 @@ print(rt.summary())
 
 Neural Additive Model (Laub, Pho, Wong 2025) adapted for insurance. One MLP subnetwork per feature, additive aggregation, Poisson/Tweedie/Gamma losses, and Dykstra-projected monotonicity constraints. Beats GLMs on deviance metrics while producing per-feature shape functions that a pricing team can actually inspect.
 
-Requires the `[neural]` extra: `pip install "insurance-gam[neural]"`
+Requires the `[neural]` extra: `uv add "insurance-gam[neural]"`
 
 ```python
 import numpy as np
@@ -146,7 +146,7 @@ shapes["vehicle_age"].plot()
 
 Neural GA2M (Richman, Scognamiglio, Wüthrich 2025). The prediction decomposes as a sum of pairwise interaction terms — one shared network serving all feature pairs, differentiated by learned interaction tokens. Diagonal terms recover main effects. Captures interactions a GLM would miss while keeping the output interpretable as a sum of 2D shape functions.
 
-Requires the `[neural]` extra: `pip install "insurance-gam[neural]"`
+Requires the `[neural]` extra: `uv add "insurance-gam[neural]"`
 
 ```python
 import numpy as np
@@ -187,25 +187,25 @@ effects = model.main_effects(df)
 ## Installation
 
 ```bash
-pip install insurance-gam
+uv add insurance-gam
 ```
 
 With neural subpackages (requires PyTorch):
 
 ```bash
-pip install "insurance-gam[neural]"
+uv add "insurance-gam[neural]"
 ```
 
 With EBM subpackage (requires interpretML):
 
 ```bash
-pip install "insurance-gam[ebm]"
+uv add "insurance-gam[ebm]"
 ```
 
 Everything:
 
 ```bash
-pip install "insurance-gam[all]"
+uv add "insurance-gam[all]"
 ```
 
 ## Design rationale