Fix docs

doc/GTSAM-Concepts.md

@@ -28,7 +28,7 @@ The core operations for a manifold are:
 
 These operations must be inverses of each other: `p.retract(p.localCoordinates(q))` should be equal to `q`.
 
-For a detailed guide on creating a new `Manifold` type, see {doc}`../gtsam/base/doc/Manifold.md`.
+For a detailed guide on creating a new `Manifold` type, see [Manifold](../gtsam/base/doc/Manifold.md).
 
 ## Group
 
@@ -38,7 +38,7 @@ Key operations are `compose`, `inverse`, and `between`. GTSAM distinguishes betw
 *   **Multiplicative Groups**: Use `operator*` (e.g., rotations, poses).
 *   **Additive Groups**: Use `operator+` (e.g., vectors).
 
-For a detailed guide on creating a `Group` type, see {doc}`../gtsam/base/doc/Group.md`.
+For a detailed guide on creating a `Group` type, see [Group](../gtsam/base/doc/Group.md).
 
 ## Lie Group
 
@@ -48,14 +48,14 @@ Lie groups have a special identity element, which allows for defining global `Ex
 
 Most Lie groups in GTSAM are also **Matrix Lie Groups**, which have an underlying matrix representation. These require additional Lie algebra operations like `Hat` and `Vee`.
 
-*   For a guide on creating a `LieGroup`, see {doc}`../gtsam/base/doc/LieGroup.md`.
-*   For matrix Lie groups, also see {doc}`../gtsam/base/doc/MatrixLieGroup.md`.
+*   For a guide on creating a `LieGroup`, see [LieGroup](../gtsam/base/doc/LieGroup.md).
+*   For matrix Lie groups, also see [MatrixLieGroup](../gtsam/base/doc/MatrixLieGroup.md).
 
 ## Vector Space
 
 A `VectorSpace` is a specialized `AdditiveGroup` that also supports scalar multiplication, a dot product, and the calculation of a norm. This concept should be satisfied by types that behave like mathematical vectors. In GTSAM, vector spaces are the foundation for tangent spaces on manifolds.
 
-For a detailed guide, see {doc}`../gtsam/base/doc/VectorSpace.md`.
+For a detailed guide, see [VectorSpace](../gtsam/base/doc/VectorSpace.md).
 
 ## Overview
 
@@ -141,4 +141,4 @@ When a Lie group acts on a space, we have two derivatives to care about:
 
   *   `gtsam::manifold::traits<T>::act(g,p,Hg,Hp)`, if the space acted upon is a continuous differentiable manifold.
 
-An example is a *similarity transform* in 3D, which can act on 3D space. The derivative in `p`, `Hp`, depends on the group element `g`. The derivative in `g`, `Hg`, is in general more complex.
+An example is a *similarity transform* in 3D, which can act on 3D space. The derivative in `p`, `Hp`, depends on the group element `g`. The derivative in `g`, `Hg`, is in general more complex.

gtsam/nonlinear/doc/ConcentratedGaussian.ipynb

@@ -13,7 +13,7 @@
         "2. EKF users receiving `ConcentratedGaussian` outputs.\n",
         "3. Advanced users leveraging transport, reset, and fusion of Left Extended Concentrated Gaussians (L-ECGs).\n",
         "\n",
-        "Related notebooks: {doc}`PriorFactor <PriorFactor.ipynb>`, {doc}`ExtendedPriorFactor <ExtendedPriorFactor.ipynb>`."
+        "Related notebooks: [PriorFactor](PriorFactor.ipynb), [ExtendedPriorFactor](ExtendedPriorFactor.ipynb)."
       ]
     },
     {
@@ -52802,4 +52802,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}

gtsam/nonlinear/doc/ExtendedPriorFactor.ipynb

@@ -9,7 +9,7 @@
         "\n",
         "## Purpose and Audience\n",
         "\n",
-        "`ExtendedPriorFactor` is the generalized building block that underlies {doc}`PriorFactor <PriorFactor.ipynb>` and other soft anchoring mechanisms. It lets you express an (optionally shifted) Gaussian (or robust) likelihood in the tangent space of an arbitrary manifold value type. This notebook targets advanced GTSAM users designing custom factors, experimenting with non-zero tangent-space means, or working with non-traditional manifold types."
+        "`ExtendedPriorFactor` is the generalized building block that underlies [PriorFactor](PriorFactor.ipynb) and other soft anchoring mechanisms. It lets you express an (optionally shifted) Gaussian (or robust) likelihood in the tangent space of an arbitrary manifold value type. This notebook targets advanced GTSAM users designing custom factors, experimenting with non-zero tangent-space means, or working with non-traditional manifold types."
       ]
     },
     {
@@ -241,7 +241,7 @@
       "source": [
         "## When Not to Use\n",
         "\n",
-        "- If you simply need a zero-mean soft prior: prefer {doc}`PriorFactor <PriorFactor.ipynb>`.\n",
+        "- If you simply need a zero-mean soft prior: prefer [PriorFactor](PriorFactor.ipynb).\n",
         "- If you require a hard equality constraint: consider `NonlinearEquality`.\n",
         "- If the residual should depend on another variable or measurement, design a custom factor instead of embedding complexity in `Local`."
       ]
@@ -253,7 +253,7 @@
       "source": [
         "## See Also\n",
         "\n",
-        "- {doc}`PriorFactor <PriorFactor.ipynb>`\n",
+        "- [PriorFactor](PriorFactor.ipynb)\n",
         "- Other manifold priors: `BetweenFactor`, `NonlinearEquality`, and robust variants\n",
         "- Underlying noise models: `noiseModel::Isotropic`, `noiseModel::Diagonal`, robust creators"
       ]
@@ -280,4 +280,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}