docs: fix typos in docstrings

lymph/models/bilateral.py

@@ -243,7 +243,7 @@ def get_spread_params(
         symmetric, the leading ``ipsi_`` or ``contra_`` is omitted, since it's valid
         for both sides.
 
-        This is consistent with how the :py:meth:`~lymph.models.Bilteral.set_params`
+        This is consistent with how the :py:meth:`.set_params`
         method expects the keyword arguments in case of the symmetry configurations.
 
         >>> model = Bilateral(graph_dict={
@@ -564,7 +564,7 @@ def likelihood(
         raise ValueError("Invalid mode. Must be either 'HMM' or 'BN'.")
 
 
-    def comp_posterior_joint_state_dist(
+    def posterior_joint_state_dist(
         self,
         given_params: types.ParamsType | None = None,
         given_diagnoses: dict[str, types.DiagnoseType] | None = None,
@@ -573,7 +573,7 @@ def comp_posterior_joint_state_dist(
     ) -> np.ndarray:
         """Compute joint post. dist. over ipsi & contra states, ``given_diagnoses``.
 
-        The ``given_diagnoses`` is a dictionary storing a :py:class:`types.DiagnoseType`
+        The ``given_diagnoses`` is a dictionary storing a :py:obj:`.types.DiagnoseType`
         for the ``"ipsi"`` and ``"contra"`` side of the neck.
 
         Essentially, this is the risk for any possible combination of ipsi- and
@@ -630,7 +630,7 @@ def risk(
         The parameters can be set via the ``given_params`` and ``given_params``, both
         of which are passed to the :py:meth:`.set_params` method. The
         ``given_diagnoses`` must be a dictionary mapping the side of the neck to a
-        :py:class:`.types.DiagnoseType`.
+        :py:obj:`.types.DiagnoseType`.
 
         Note:
             The computation is much faster if no parameters are given, since then the
@@ -645,7 +645,7 @@ def risk(
                 only marginalizes over the states that match the involvement pattern.
         """
         # TODO: test this method
-        posterior_state_probs = self.comp_posterior_joint_state_dist(
+        posterior_state_probs = self.posterior_joint_state_dist(
             given_params=given_params,
             given_diagnoses=given_diagnoses,
             t_stage=t_stage,

lymph/models/midline.py

@@ -351,7 +351,8 @@ def get_params(
         """Return all the parameters of the model.
 
         This includes the spread parameters from the call to :py:meth:`get_spread_params`
-        and the distribution parameters from the call to :py:meth:`get_distribution_params`.
+        and the distribution parameters from the call to
+        :py:meth:`~.diagnose_times.Composite.get_distribution_params`.
         """
         params = {}
         params["midext_prob"] = self.midext_prob

lymph/models/unilateral.py

@@ -638,7 +638,7 @@ def state_dist(
         which is essentially a marginalization of the evolution over the possible
         states as computed by :py:meth:`.state_dist_evo` with the distribution
         over diagnose times for the given T-stage from the dictionary returned by
-        :py:meth:`.get_all_dsitributions`.
+        :py:meth:`.get_all_distributions`.
 
         Or, when ``mode`` is set to ``"BN"``, compute the distribution over states for
         the Bayesian network. In that case, the ``t_stage`` parameter is ignored.
@@ -669,12 +669,12 @@ def obs_dist(
 
         Returns an array of probabilities for each possible complete observation. This
         entails multiplying the distribution over states as returned by the
-        :py:meth:`.state_dist` method with the :py:attr:`.observation_matrix`.
+        :py:meth:`.state_dist` method with the :py:meth:`.observation_matrix`.
 
         Note that since the :py:attr:`.observation_matrix` can become very large, this
         method is not very efficient for inference. Instead, we compute the
-        :py:attr:`.diagnose_matrices` from the :py:attr:`.observation_matrix` and
-        the :py:attr:`.data_matrices` and use these to compute the likelihood.
+        :py:meth:`.diagnose_matrix` from the :py:meth:`.observation_matrix` and
+        the :py:meth:`.data_matrix` and use these to compute the likelihood.
         """
         state_dist = self.state_dist(t_stage=t_stage, mode=mode)
         return state_dist @ self.observation_matrix()

tests/binary_bilateral_test.py

@@ -318,7 +318,7 @@ def test_posterior_state_dist(self):
         random_parameters = self.create_random_params()
         random_diagnoses = self.create_random_diagnoses()
 
-        posterior = self.model.comp_posterior_joint_state_dist(
+        posterior = self.model.posterior_joint_state_dist(
             given_params=random_parameters,
             given_diagnoses=random_diagnoses,
         )