Refactor documentation formatting and improve installation instructions

.github/dependabot.yml

@@ -6,9 +6,3 @@ updates:
     schedule:
       interval: "weekly"
       day: "friday"
-
-  - package-ecosystem: "uv"
-    directory: "/"
-    schedule:
-      interval: "weekly"
-      day: "friday"

docs/index.md

@@ -28,16 +28,6 @@ $$
 where $W(t)$ is a [Wiener process](https://en.wikipedia.org/wiki/Wiener_process)
 (standard Brownian motion).
 
-## Why sdepack?
-
-| Feature | Detail |
-|---|---|
-| **Performance** | Compiled Fortran core — no Python loops in the hot path |
-| **Reproducibility** | Deterministic seeded RNG (Park-Miller LCG with Schrage's overflow protection) |
-| **Range of solvers** | Orders 1–4, covering both time-invariant and time-variant SDEs |
-| **Simplicity** | One function call per solve; solutions land in a pre-allocated NumPy array |
-| **Portability** | Linux, macOS, Windows; Python 3.10–3.14 |
-
 ## Mathematical model
 
 Within the solvers, the SDE is parameterized as:

docs/installation.md

@@ -101,18 +101,18 @@ This invokes the `meson` build system to compile the Fortran sources via
     Source builds require a working Fortran compiler. On most Linux
     distributions install `gfortran`:
 
-    ```bash
-    # Debian/Ubuntu
-    sudo apt install gfortran
+```bash
+# Debian/Ubuntu
+sudo apt install gfortran
 
-    # Fedora
-    sudo dnf install gcc-gfortran
+# Fedora
+sudo dnf install gcc-gfortran
 
-    # macOS (Homebrew)
-    brew install gcc
-    ```
+# macOS (Homebrew)
+brew install gcc
+```
 
-    On Windows, install MinGW-w64 with gfortran or use MSYS2.
+On Windows, install MinGW-w64 with gfortran or use MSYS2.
 
 ## Verifying the installation
 

docs/quickstart.md

@@ -264,16 +264,16 @@ mean 0 and variance $\sigma^2/(2\theta) = 0.125$.
 !!! tip "Plotting trajectories"
     Use `matplotlib` with the time grid for visualization:
 
-    ```python
-    import matplotlib.pyplot as plt
-
-    t = np.linspace(0.0, tn, N + 1)
-    plt.plot(t, x)
-    plt.xlabel("Time")
-    plt.ylabel("X(t)")
-    plt.title("SDE trajectory")
-    plt.show()
-    ```
+```python
+import matplotlib.pyplot as plt
+
+t = np.linspace(0.0, tn, N + 1)
+plt.plot(t, x)
+plt.xlabel("Time")
+plt.ylabel("X(t)")
+plt.title("SDE trajectory")
+plt.show()
+```
 
 !!! tip "Seed selection"
     The seed must be a positive integer. Different seeds produce entirely