Add input validation for dl1_dp1 and dl2_dp2

src/rolch/base/distribution.py

@@ -42,9 +42,21 @@ def dl2_dpp(
     ) -> np.ndarray:
         """Take the first derivative of the likelihood function with respect to both parameters."""
 
+    def _validate_dln_dpn_inputs(
+        self, y: np.ndarray, theta: np.ndarray, param: int
+    ) -> None:
+        if param >= self.n_params:
+            raise ValueError(
+                f"{self.__class__.__name__} has only {self.n_params} distribution parameters.\nYou have passed {param}. Please remember we start counting at 0."
+            )
+
     def _validate_dl2_dpp_inputs(
         self, y: np.ndarray, theta: np.ndarray, params: Tuple[int, int]
     ) -> None:
+        if max(params) >= self.n_params:
+            raise ValueError(
+                f"{self.__class__.__name__} has only {self.n_params} distribution parameters.\nYou have passed {params}. Please remember we start counting at 0."
+            )
         if params[0] == params[1]:
             raise ValueError("Cross derivatives must use different parameters.")
 

src/rolch/distributions/gamma.py

@@ -87,6 +87,7 @@ def gamlss_to_scipy(
         return alpha, loc, scale
 
     def dl1_dp1(self, y: np.ndarray, theta: np.ndarray, param: int = 0) -> np.ndarray:
+        self._validate_dln_dpn_inputs(y, theta, param)
         mu, sigma = self.theta_to_params(theta)
 
         if param == 0:
@@ -103,6 +104,7 @@ def dl1_dp1(self, y: np.ndarray, theta: np.ndarray, param: int = 0) -> np.ndarra
             )
 
     def dl2_dp2(self, y: np.ndarray, theta: np.ndarray, param: int = 0) -> np.ndarray:
+        self._validate_dln_dpn_inputs(y, theta, param)
         mu, sigma = self.theta_to_params(theta)
         if param == 0:
             # MU

src/rolch/distributions/johnsonsu.py

@@ -56,6 +56,7 @@ def theta_to_params(
         return mu, sigma, nu, tau
 
     def dl1_dp1(self, y: np.ndarray, theta: np.ndarray, param: int = 0) -> np.ndarray:
+        self._validate_dln_dpn_inputs(y, theta, param)
         mu, sigma, nu, tau = self.theta_to_params(theta)
 
         if param == 0:
@@ -91,6 +92,7 @@ def dl1_dp1(self, y: np.ndarray, theta: np.ndarray, param: int = 0) -> np.ndarra
             return dldt
 
     def dl2_dp2(self, y: np.ndarray, theta: np.ndarray, param: int = 0) -> np.ndarray:
+        self._validate_dln_dpn_inputs(y, theta, param)
         mu, sigma, nu, tau = self.theta_to_params(theta)
         if param == 0:
             # MU

src/rolch/distributions/normal.py

@@ -30,6 +30,7 @@ def theta_to_params(self, theta: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
         return mu, sigma
 
     def dl1_dp1(self, y: np.ndarray, theta: np.ndarray, param: int = 0) -> np.ndarray:
+        self._validate_dln_dpn_inputs(y, theta, param)
         mu, sigma = self.theta_to_params(theta)
 
         if param == 0:
@@ -39,6 +40,7 @@ def dl1_dp1(self, y: np.ndarray, theta: np.ndarray, param: int = 0) -> np.ndarra
             return ((y - mu) ** 2 - sigma**2) / (sigma**3)
 
     def dl2_dp2(self, y: np.ndarray, theta: np.ndarray, param: int = 0) -> np.ndarray:
+        self._validate_dln_dpn_inputs(y, theta, param)
         _, sigma = self.theta_to_params(theta)
         if param == 0:
             # MU

src/rolch/distributions/studentt.py

@@ -43,6 +43,7 @@ def theta_to_params(
         return mu, sigma, nu
 
     def dl1_dp1(self, y: np.ndarray, theta: np.ndarray, param: int = 0) -> np.ndarray:
+        self._validate_dln_dpn_inputs(y, theta, param)
         mu, sigma, nu = self.theta_to_params(theta)
 
         if param == 0:
@@ -73,6 +74,7 @@ def dl1_dp1(self, y: np.ndarray, theta: np.ndarray, param: int = 0) -> np.ndarra
             ) / 2
 
     def dl2_dp2(self, y: np.ndarray, theta: np.ndarray, param: int = 0) -> np.ndarray:
+        self._validate_dln_dpn_inputs(y, theta, param)
         _, sigma, nu = self.theta_to_params(theta)
         if param == 0:
             # MU