advance to version 0.0.6

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = limetr
-version = 0.0.5
+version = 0.0.6
 description =
 long_description = file: README.rst
 long_description_content_type = text/x-rst
@@ -15,7 +15,7 @@ zip_safe = False
 install_requires =
     numpy
     scipy
-    spmat
+    spmat==0.0.9
 include_package_data = True
 packages = find:
 package_dir = = src

src/limetr/__init__.py

@@ -277,7 +277,7 @@ def objective(self, x: NDArray) -> float:
 
         val = 0.5*self.N*np.log(2.0*np.pi)
 
-        D = BDLMat(diags=V, lmats=Z*np.sqrt(gamma), dsizes=self.n)
+        D = BDLMat(dvecs=V, lmats=Z*np.sqrt(gamma), dsizes=self.n)
         val += 0.5*D.logdet()
         val += 0.5*R.dot(D.invdot(R))
 
@@ -302,7 +302,7 @@ def gradient(self, x: NDArray) -> NDArray:
 
         # residual and variance
         R = Y - F_beta
-        D = BDLMat(diags=V, lmats=Z*np.sqrt(gamma), dsizes=self.n)
+        D = BDLMat(dvecs=V, lmats=Z*np.sqrt(gamma), dsizes=self.n)
 
         # gradient for beta
         DR = D.invdot(R)
@@ -336,7 +336,7 @@ def hessian(self, x: NDArray) -> NDArray:
         _, JF_beta, _, Z, V = self._get_nll_components(beta)
 
         sqrt_gamma = np.sqrt(gamma)
-        d = BDLMat(diags=V, lmats=Z*np.sqrt(gamma), dsizes=self.n)
+        d = BDLMat(dvecs=V, lmats=Z*np.sqrt(gamma), dsizes=self.n)
 
         split_idx = np.cumsum(self.n)[:-1]
         v_study = np.split(V, split_idx)
@@ -545,7 +545,7 @@ def get_varmat(model: LimeTr):
     Z = model.Z*np.sqrt(model.w)[:, None]
     n = model.n
     gamma = model.gamma
-    return BDLMat(diags=S**2, lmats=Z*np.sqrt(gamma), dsizes=n)
+    return BDLMat(dvecs=S**2, lmats=Z*np.sqrt(gamma), dsizes=n)
 
 
 def get_marginal_rvar(model: LimeTr):