diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml
index be77c3d..09a62ae 100644
--- a/.github/workflows/deploy.yml
+++ b/.github/workflows/deploy.yml
@@ -19,9 +19,7 @@ jobs:
 
             - name: Install dependencies
               run: |
-                  pip install -r requirements.txt
-                  pip install mkdocs-material mkdocs-material-extensions mkdocstrings[python]
-                  pip install -e .
+                  pip install -e ".[dev]"
 
             - name: Build and Deploy
               working-directory: docs # Set the working directory to 'docs'
diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
index 57d03f5..5e22c1d 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/python-package.yml
@@ -27,7 +27,7 @@ jobs:
               run: |
                   python -m pip install --upgrade pip
                   python -m pip install flake8 pytest
-                  if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+                  pip install -e .
             - name: Lint with flake8
               run: |
                   # stop the build if there are Python syntax errors or undefined names
diff --git a/examples/T4-regression.ipynb b/examples/T4-regression.ipynb
index 29358bd..6d1b3c4 100644
--- a/examples/T4-regression.ipynb
+++ b/examples/T4-regression.ipynb
@@ -9,9 +9,168 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
+    "from pycircstat2 import load_data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear-Circular Regression\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pycircstat2.regression import LCRegression\n",
     "\n",
-    "from pycircstat2 import load_data\n",
-    "from pycircstat2.regression import CLRegression"
+    "df = load_data(\"lung_deaths\", source=\"pewsey\")\n",
+    "df[\"θ\"] = (np.pi / 6) * df[\"month\"].to_numpy()\n",
+    "df = df.rename(columns={\"deaths\": \"y\"})\n",
+    "df = df[~((df[\"month\"] == 2) & df[\"year\"].isin([1976, 1979]))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Linear-Circular Regression\n",
+      "\n",
+      "Formula: y ~ cos(θ) + sin(θ)\n",
+      "\n",
+      "Residuals:\n",
+      "    Min      1Q Median     3Q    Max\n",
+      "-402.64 -121.16 -15.45 136.53 459.72\n",
+      "\n",
+      "Coefficients:\n",
+      "             Estimate  Std. Error  CI[2.5%]  CI[97.5]%  t value  Pr(>|t|)\n",
+      "(Intercept)   2122.26       22.37    2077.6     2166.9    94.86    <2e-16  ***\n",
+      "cos(θ)         451.32       31.41     388.6      514.0    14.37    <2e-16  ***\n",
+      "sin(θ)         597.02       31.87     533.4      660.6    18.73    <2e-16  ***\n",
+      "---\n",
+      "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
+      "\n",
+      "n = 70, p = 3, Residual SE = 187.024 on 67 DF\n",
+      "R-Squared = 0.8904, adjusted R-Squared = 0.8871\n",
+      "F-statistics = 272.0277 on 2 and 67 DF, p-value: 6.915228e-33\n",
+      "\n",
+      "Log Likelihood = -463.9790, AIC = 935.9581, BIC = 944.9521\n",
+      "\n",
+      "Harmonic decomposition:\n",
+      "term    amplitude  SE       CI[2.5%, 97.5%]       phase   SE      CI[2.5%, 97.5%] \n",
+      "----------------------------------------------------------------------------------\n",
+      "θ, k=1  748.4167   32.0873  [685.5267, 811.3066]  0.9235  0.0417  [0.8418, 1.0052]\n",
+      "Phase in radians; SEs and CIs from the delta method on (cos, sin) coefficients.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x450 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lc = LCRegression(formula=\"y ~ cos(θ) + sin(θ)\", data=df)\n",
+    "lc.plot()\n",
+    "lc.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# or check the lm.plot()\n",
+    "_ = lc.lm_fit.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Linear-Circular Regression\n",
+      "User formula:     y ~ harmonic(θ, k=2)\n",
+      "Expanded formula: y ~ cos(θ) + sin(θ) + cos(2*θ) + sin(2*θ)\n",
+      "\n",
+      "Formula: y ~ cos(θ) + sin(θ) + cos(2*θ) + sin(2*θ)\n",
+      "\n",
+      "Residuals:\n",
+      "     Min      1Q Median     3Q     Max\n",
+      "-446.942 -90.878  1.259 84.141 447.736\n",
+      "\n",
+      "Coefficients:\n",
+      "             Estimate  Std. Error  CI[2.5%]  CI[97.5]%   t value  Pr(>|t|)\n",
+      "(Intercept)  2125.179      20.670   2083.90    2166.46  102.8167   < 2e-16  ***\n",
+      "cos(θ)        454.239      29.002    396.32     512.16   15.6624   < 2e-16  ***\n",
+      "sin(θ)        602.073      29.459    543.24     660.91   20.4379   < 2e-16  ***\n",
+      "cos(2 * θ)     -4.012      29.002    -61.93      53.91   -0.1383  0.890396\n",
+      "sin(2 * θ)    108.804      29.459     49.97     167.64    3.6934  0.000455  ***\n",
+      "---\n",
+      "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
+      "\n",
+      "n = 70, p = 5, Residual SE = 172.625 on 65 DF\n",
+      "R-Squared = 0.9094, adjusted R-Squared = 0.9038\n",
+      "F-statistics = 163.0603 on 4 and 65 DF, p-value: 3.941299e-33\n",
+      "\n",
+      "Log Likelihood = -457.3105, AIC = 926.6210, BIC = 940.1119\n",
+      "\n",
+      "Harmonic decomposition:\n",
+      "term    amplitude  SE       CI[2.5%, 97.5%]       phase   SE      CI[2.5%, 97.5%] \n",
+      "----------------------------------------------------------------------------------\n",
+      "θ, k=1  754.2047   29.6710  [696.0507, 812.3587]  0.9244  0.0382  [0.8496, 0.9993]\n",
+      "θ, k=2  108.8776   29.4870  [51.0842, 166.6710]   1.6077  0.2661  [1.0861, 2.1292]\n",
+      "Phase in radians; SEs and CIs from the delta method on (cos, sin) coefficients.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x450 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lc_ext = LCRegression(\"y ~ harmonic(θ, k=2)\", df)\n",
+    "lc_ext.plot()\n",
+    "lc_ext.summary()"
    ]
   },
   {
@@ -25,7 +184,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pycircstat2.regression import CLRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -54,7 +222,7 @@
       "Coefficients for Mean Direction (Beta):\n",
       "\n",
       "      Estimate     Std. Error   t value    Pr(>|t|)\n",
-      "β0   -0.00832     0.00136      6.12       0.00000     ***\n",
+      "β0   -0.00832     0.00136      -6.12      0.00000     ***\n",
       "\n",
       "Summary:\n",
       "  Mean Direction (mu) in radians:\n",
@@ -74,18 +242,29 @@
       "p-values are approximated using the normal distribution.\n",
       "\n"
      ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "# Fit the mean direction only model\n",
     "# This implmentation is ported from R package circular's lm.circular.cl and we arrive at the same results\n",
     "cl_reg_mean = CLRegression(formula=\"θ ~ X\", model_type=\"mean\", data=data_cl, tol=1e-10)\n",
+    "cl_reg_mean.plot()\n",
     "cl_reg_mean.summary()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -111,38 +290,38 @@
       "\n",
       "  Concentration Parameter (kappa):\n",
       "    Index    kappa        Std. Error\n",
-      "    [1]      27.23391       6.85109\n",
-      "    [2]       3.58005       0.80650\n",
-      "    [3]       2.32321       0.50677\n",
-      "    [4]       7.19881       1.75046\n",
-      "    [5]      44.85424      11.32782\n",
-      "    [6]       7.69401       1.87768\n",
-      "    [7]       3.24004       0.71988\n",
-      "    [8]       2.10257       0.46255\n",
-      "    [9]       1.15536       0.31770\n",
-      "    [10]       1.78040       0.40459\n",
-      "    [11]       2.65383       0.57893\n",
-      "    [12]       7.69401       1.87768\n",
-      "    [13]       0.91536       0.29398\n",
-      "    [14]      12.25746       3.04289\n",
-      "    [15]       7.44229       1.81305\n",
-      "    [16]       2.74359       0.59957\n",
-      "    [17]       1.55860       0.36972\n",
-      "    [18]       0.80133       0.28463\n",
-      "    [19]       8.22329       2.01335\n",
-      "    [20]       5.70344       1.36357\n",
-      "    [21]       8.22329       2.01335\n",
-      "    [22]       8.22329       2.01335\n",
-      "    [23]       5.16176       1.22191\n",
-      "    [24]       4.51870       1.05263\n",
-      "    [25]       2.74359       0.59957\n",
-      "    [26]       7.95425       1.94442\n",
-      "    [27]       0.97833       0.29967\n",
-      "    [28]       3.82633       0.87048\n",
-      "    [29]       0.97833       0.29967\n",
-      "    [30]       1.55860       0.36972\n",
-      "    [31]       1.90288       0.42564\n",
-      "    Mean:    6.36210 (SE: 1.56042)\n",
+      "    [1]      27.23391      15.72579\n",
+      "    [2]       3.58005       0.87437\n",
+      "    [3]       2.32321       0.65714\n",
+      "    [4]       7.19881       2.10099\n",
+      "    [5]      44.85424      31.52657\n",
+      "    [6]       7.69401       2.32574\n",
+      "    [7]       3.24004       0.80607\n",
+      "    [8]       2.10257       0.62597\n",
+      "    [9]       1.15536       0.47820\n",
+      "    [10]       1.78040       0.58043\n",
+      "    [11]       2.65383       0.70614\n",
+      "    [12]       7.69401       2.32574\n",
+      "    [13]       0.91536       0.42718\n",
+      "    [14]      12.25746       4.79550\n",
+      "    [15]       7.44229       2.21016\n",
+      "    [16]       2.74359       0.72019\n",
+      "    [17]       1.55860       0.54739\n",
+      "    [18]       0.80133       0.39901\n",
+      "    [19]       8.22329       2.57720\n",
+      "    [20]       5.70344       1.49437\n",
+      "    [21]       8.22329       2.57720\n",
+      "    [22]       8.22329       2.57720\n",
+      "    [23]       5.16176       1.30606\n",
+      "    [24]       4.51870       1.10815\n",
+      "    [25]       2.74359       0.72019\n",
+      "    [26]       7.95425       2.44799\n",
+      "    [27]       0.97833       0.44154\n",
+      "    [28]       3.82633       0.92919\n",
+      "    [29]       0.97833       0.44154\n",
+      "    [30]       1.55860       0.54739\n",
+      "    [31]       1.90288       0.59792\n",
+      "    Mean:    6.36210\n",
       "\n",
       "Model Fit Metrics:\n",
       "\n",
@@ -155,6 +334,16 @@
       "p-values are approximated using the normal distribution.\n",
       "\n"
      ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -162,7 +351,7 @@
     "cl_reg_kappa = CLRegression(\n",
     "    formula=\"θ ~ X\", model_type=\"kappa\", data=data_cl, tol=1e-10\n",
     ")\n",
-    "\n",
+    "cl_reg_kappa.plot()\n",
     "cl_reg_kappa.summary()"
    ]
   },
@@ -175,7 +364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -191,64 +380,82 @@
       "Coefficients for Mean Direction (Beta):\n",
       "\n",
       "      Estimate     Std. Error   t value    Pr(>|t|)\n",
-      "β0   -0.00376     0.00027      13.84      0.00000     ***\n",
+      "β0   -0.00454     0.00029      -15.58     0.00000     ***\n",
       "\n",
       "Coefficients for Concentration (Gamma):\n",
       "\n",
       "      Estimate     Std. Error   t value    Pr(>|t|)    \n",
-      "α      -0.41403     0.51485      -0.80      0.42130     \n",
-      "γ0     0.04590      0.00920      4.99       0.00000     ***\n",
+      "α      -0.37595     0.50996      -0.74      0.46100     \n",
+      "γ0     0.04536      0.00914      4.97       0.00000     ***\n",
       "\n",
       "Summary:\n",
       "  Mean Direction (mu) in radians:\n",
-      "    μ: 1.91714 (SE: 0.04552)\n",
+      "    μ: 2.03831 (SE: 0.04581)\n",
       "\n",
       "  Concentration Parameter (kappa):\n",
       "    Index    kappa        Std. Error\n",
-      "    [1]      89.82281      22.75070\n",
-      "    [2]       5.46322       1.30087\n",
-      "    [3]       3.00825       0.66257\n",
-      "    [4]      14.32354       3.56887\n",
-      "    [5]     178.80833      45.35346\n",
-      "    [6]      15.70063       3.91924\n",
-      "    [7]       4.76046       1.11638\n",
-      "    [8]       2.62129       0.57155\n",
-      "    [9]       1.14739       0.31683\n",
-      "    [10]       2.08376       0.45894\n",
-      "    [11]       3.61450       0.81540\n",
-      "    [12]      15.70063       3.91924\n",
-      "    [13]       0.83211       0.28703\n",
-      "    [14]      29.85278       7.51655\n",
-      "    [15]      14.99629       3.74005\n",
-      "    [16]       3.78426       0.85950\n",
-      "    [17]       1.73426       0.39698\n",
-      "    [18]       0.69254       0.27687\n",
-      "    [19]      17.21012       4.30318\n",
-      "    [20]      10.38763       2.56634\n",
-      "    [21]      17.21012       4.30318\n",
-      "    [22]      17.21012       4.30318\n",
-      "    [23]       9.05141       2.22521\n",
-      "    [24]       7.53325       1.83642\n",
-      "    [25]       3.78426       0.85950\n",
-      "    [26]      16.43806       4.10682\n",
-      "    [27]       0.91211       0.29370\n",
-      "    [28]       5.98847       1.43774\n",
-      "    [29]       0.91211       0.29370\n",
-      "    [30]       1.73426       0.39698\n",
-      "    [31]       2.28409       0.49869\n",
-      "    Mean:    16.11623 (SE: 4.04050)\n",
+      "    [1]      88.02465      51.59362\n",
+      "    [2]       5.53279       1.37493\n",
+      "    [3]       3.06798       0.87850\n",
+      "    [4]      14.34269       4.27055\n",
+      "    [5]     173.81788     123.88537\n",
+      "    [6]      15.70469       4.84196\n",
+      "    [7]       4.82888       1.22087\n",
+      "    [8]       2.67766       0.80628\n",
+      "    [9]       1.18350       0.49407\n",
+      "    [10]       2.13431       0.70293\n",
+      "    [11]       3.67833       0.99220\n",
+      "    [12]      15.70469       4.84196\n",
+      "    [13]       0.86153       0.40538\n",
+      "    [14]      29.63604      11.80100\n",
+      "    [15]      15.00825       4.54660\n",
+      "    [16]       3.84902       1.02467\n",
+      "    [17]       1.78017       0.63118\n",
+      "    [18]       0.71858       0.36072\n",
+      "    [19]      17.19602       5.49535\n",
+      "    [20]      10.44084       2.79201\n",
+      "    [21]      17.19602       5.49535\n",
+      "    [22]      17.19602       5.49535\n",
+      "    [23]       9.11249       2.35280\n",
+      "    [24]       7.60046       1.90076\n",
+      "    [25]       3.84902       1.02467\n",
+      "    [26]      16.43345       5.15779\n",
+      "    [27]       0.94334       0.42930\n",
+      "    [28]       6.05819       1.49804\n",
+      "    [29]       0.94334       0.42930\n",
+      "    [30]       1.78017       0.63118\n",
+      "    [31]       2.33699       0.74215\n",
+      "    Mean:    15.92381\n",
       "\n",
       "Model Fit Metrics:\n",
       "\n",
       "Metric       Value       \n",
-      "nLL          -37.81772   \n",
-      "AIC          -69.63544   \n",
-      "BIC          -65.33348   \n",
+      "nLL          -38.01069   \n",
+      "AIC          -70.02138   \n",
+      "BIC          -65.71942   \n",
       "\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "p-values are approximated using the normal distribution.\n",
       "\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/ziweih/Works/pycircstat2/pycircstat2/regression.py:165: RuntimeWarning: CLRegression did not converge in 100 iterations (last diff=3.04e-05, tol=1.00e-10).\n",
+      "  self.result = self._fit()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -264,28 +471,35 @@
     "    alpha0=alpha0,\n",
     "    gamma0=gamma0,\n",
     ")\n",
-    "\n",
+    "cl_reg_mixed.plot()\n",
     "cl_reg_mixed.summary()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x11fa8a930>"
+       "<matplotlib.legend.Legend at 0x11fc5d590>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x500 with 2 Axes>"
       ]
@@ -439,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -481,7 +695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -492,36 +706,63 @@
       "Circular-Circular Regression\n",
       "\n",
       "Circular Correlation Coefficient (rho): 0.51234\n",
+      "Mean Residual Cosine (A_k):             0.45009\n",
+      "Residual Concentration (kappa):         1.00676\n",
       "\n",
-      "Coefficients:\n",
-      "Harmonic     Cosine Coeff   Sine Coeff    \n",
-      "(Intercept)  0.03765        -0.23775      \n",
-      "cos(x1,k=1)  0.47746        -0.28901      \n",
-      "sin(x1,k=1)  0.26616        0.23117       \n",
+      "Coefficients (Cosine Model):\n",
+      "\n",
+      "             Estimate     Std. Error   t value    Pr(>|t|)    \n",
+      "(Intercept)  0.03765      0.14388      0.26       0.79654     \n",
+      "cos(x1,k=1)  0.47746      0.25193      1.90       0.07424     .\n",
+      "sin(x1,k=1)  0.26616      0.17887      1.49       0.15406     \n",
       "\n",
-      "P-values for Higher-Order Terms:\n",
-      "p1: 0.15187, p2: 0.20656\n",
+      "Coefficients (Sine Model):\n",
+      "\n",
+      "             Estimate     Std. Error   t value    Pr(>|t|)    \n",
+      "(Intercept)  -0.23775     0.14393      -1.65      0.11590     \n",
+      "cos(x1,k=1)  -0.28901     0.25201      -1.15      0.26647     \n",
+      "sin(x1,k=1)  0.23117      0.17893      1.29       0.21270     \n",
+      "\n",
+      "Higher-Order Terms Test:\n",
+      "\n",
+      "             Pr(>χ²)     \n",
+      "cosine model 0.15187     \n",
+      "sine model   0.20656     \n",
       "\n",
       "Higher-order terms are not significant at the 0.05 level.\n",
+      "\n",
+      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
+      "Per-coefficient p-values use the t distribution; the higher-order test uses χ² (Jammalamadaka & Sengupta 2001).\n",
       "\n"
      ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "cc = CCRegression(formula=\"theta ~ psi\", data=df_rad)\n",
+    "cc.plot()\n",
     "cc.summary()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "A.k=0.4500934560546159; kappa=1.0067620166810398\n"
+      "A.k=0.45009345605461604; kappa=1.00676201668104\n"
      ]
     }
    ],
@@ -569,25 +810,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: 2025-11-03 14:19:13CET\n",
+      "Last updated: 2026-05-05 16:12:37 CEST\n",
       "\n",
       "Python implementation: CPython\n",
-      "Python version       : 3.12.12\n",
-      "IPython version      : 9.6.0\n",
+      "Python version       : 3.13.11\n",
+      "IPython version      : 9.13.0\n",
       "\n",
-      "pycircstat2: 0.1.15\n",
-      "matplotlib : 3.10.7\n",
-      "pandas     : 2.3.3\n",
-      "numpy      : 2.3.4\n",
+      "matplotlib : 3.10.9\n",
+      "numpy      : 2.4.4\n",
+      "pandas     : 3.0.2\n",
+      "pycircstat2: 0.1.16\n",
       "\n",
-      "Watermark: 2.5.0\n",
+      "Watermark: 2.6.0\n",
       "\n"
      ]
     }
@@ -600,7 +841,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv (3.12.12)",
+   "display_name": "pycircstat2 (3.13.11)",
    "language": "python",
    "name": "python3"
   },
@@ -614,7 +855,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.12"
+   "version": "3.13.11"
   }
  },
  "nbformat": 4,
diff --git a/pycircstat2/data/pewsey/lung_deaths.csv b/pycircstat2/data/pewsey/lung_deaths.csv
new file mode 100644
index 0000000..a65a83a
--- /dev/null
+++ b/pycircstat2/data/pewsey/lung_deaths.csv
@@ -0,0 +1,73 @@
+,year,month,deaths
+1,1974,1,3035
+2,1974,2,2552
+3,1974,3,2704
+4,1974,4,2554
+5,1974,5,2014
+6,1974,6,1655
+7,1974,7,1721
+8,1974,8,1524
+9,1974,9,1596
+10,1974,10,2074
+11,1974,11,2199
+12,1974,12,2512
+13,1975,1,2933
+14,1975,2,2889
+15,1975,3,2938
+16,1975,4,2497
+17,1975,5,1870
+18,1975,6,1726
+19,1975,7,1607
+20,1975,8,1545
+21,1975,9,1396
+22,1975,10,1787
+23,1975,11,2076
+24,1975,12,2837
+25,1976,1,2787
+26,1976,2,3891
+27,1976,3,3179
+28,1976,4,2011
+29,1976,5,1636
+30,1976,6,1580
+31,1976,7,1489
+32,1976,8,1300
+33,1976,9,1356
+34,1976,10,1653
+35,1976,11,2013
+36,1976,12,2823
+37,1977,1,2996
+38,1977,2,2523
+39,1977,3,2540
+40,1977,4,2520
+41,1977,5,1994
+42,1977,6,1641
+43,1977,7,1691
+44,1977,8,1479
+45,1977,9,1696
+46,1977,10,1877
+47,1977,11,2032
+48,1977,12,2484
+49,1978,1,2899
+50,1978,2,2990
+51,1978,3,2890
+52,1978,4,2379
+53,1978,5,1933
+54,1978,6,1734
+55,1978,7,1617
+56,1978,8,1495
+57,1978,9,1440
+58,1978,10,1777
+59,1978,11,1970
+60,1978,12,2745
+61,1979,1,2841
+62,1979,2,3535
+63,1979,3,3010
+64,1979,4,2091
+65,1979,5,1667
+66,1979,6,1589
+67,1979,7,1518
+68,1979,8,1349
+69,1979,9,1392
+70,1979,10,1619
+71,1979,11,1954
+72,1979,12,2633
diff --git a/pycircstat2/data/pewsey/lung_deaths.csv-metadata.json b/pycircstat2/data/pewsey/lung_deaths.csv-metadata.json
new file mode 100644
index 0000000..6c9be99
--- /dev/null
+++ b/pycircstat2/data/pewsey/lung_deaths.csv-metadata.json
@@ -0,0 +1,25 @@
+{
+    "Description": "Monthly numbers of deaths attributed to lung disease in a given area, recorded over six consecutive years (1974-1979). Used in Pewsey, Neuhäuser & Ruxton (2014) to illustrate linear-circular regression with a periodic (annual) seasonal component. The February 1976 (3891) and February 1979 (3535) observations are typically treated as outliers and dropped before fitting the cosine regression model in §8.4.1-§8.4.2.",
+    "Source": "Hand et al. (1994) — A Handbook of Small Data Sets, Chapman & Hall.",
+    "Examples": "Pewsey, Neuhäuser & Ruxton (2014) §8.4.1-§8.4.4",
+    "Columns": {
+        "year": {
+            "name": "year",
+            "type": "integer",
+            "unit": "calendar year",
+            "description": "calendar year of observation (1974-1979)"
+        },
+        "month": {
+            "name": "month",
+            "type": "integer",
+            "unit": "month-of-year",
+            "description": "month index, 1=January through 12=December"
+        },
+        "deaths": {
+            "name": "deaths",
+            "type": "integer",
+            "unit": "count",
+            "description": "monthly number of deaths attributed to lung disease"
+        }
+    }
+}
diff --git a/pycircstat2/regression.py b/pycircstat2/regression.py
index 53410f6..aa466ce 100644
--- a/pycircstat2/regression.py
+++ b/pycircstat2/regression.py
@@ -1,14 +1,18 @@
+import re
+import warnings
 from typing import Iterable, List, Optional, Tuple, Union
 
 import numpy as np
 import pandas as pd
+import polars as pl
+from hea import lm as _hea_lm
 from scipy.linalg import lstsq
-from scipy.special import i0, i1
-from scipy.stats import chi2, norm
+from scipy.special import i0e
+from scipy.stats import chi2, norm, t as student_t
 
 from .utils import A1, A1inv, significance_code
 
-__all__ = ["CLRegression", "CCRegression"]
+__all__ = ["CLRegression", "CCRegression", "LCRegression"]
 
 
 def _safe_solve(matrix: np.ndarray, rhs: np.ndarray) -> np.ndarray:
@@ -67,27 +71,45 @@ class CLRegression:
         A dictionary containing the estimated coefficients and other statistics.
 
         - beta : np.ndarray
-            Estimated beta coefficients for the mean direction.
+            Estimated beta coefficients for the mean direction. Used by
+            'mean' and 'mixed' models; zero for 'kappa'.
         - alpha : float
-            Estimated intercept for the concentration parameter..
+            Estimated intercept for the concentration parameter.
         - gamma : np.ndarray
             Estimated coefficients for the concentration parameter.
         - mu : float
             Estimated mean direction of the circular response.
-        - kappa : float
-            Estimated concentration parameter of the circular response.
+        - kappa : float or np.ndarray
+            Concentration parameter. Scalar for 'mean'; n-element array
+            of per-observation values κ_i = exp(α + X_iᵀγ) for 'kappa'
+            and 'mixed'.
         - log_likelihood : float
             Log-likelihood of the model.
 
     Methods
     -------
     summary()
-        Print a summary of the regression results.
-
+        Print the coefficient table, mean direction, concentration, and fit
+        metrics.
+    predict(X_new)
+        Predict mean direction at new X (constant μ for ``model_type='kappa'``).
+    predict_kappa(X_new)
+        Predict per-observation κ̂(X) for ``model_type`` in
+        ``{'kappa', 'mixed'}``.
+    plot(figsize=None, n_curve=200, axes=None)
+        Two-panel diagnostic figure (fit overlay / κ curve / residuals,
+        depending on ``model_type`` and dimensionality).
+    AIC(), BIC()
+        Information criteria for the fitted model.
 
     Notes
     -----
-    The implementation is ported from the `lm.circular.cl` in the `circular` R package.
+    The 'mean' branch is ported from ``lm.circular.cl`` in the ``circular``
+    R package (Agostinelli & Lund); SE formulas follow Fisher (1993)
+    eq. 6.62-6.64. The 'kappa' and 'mixed' branches extend that framework
+    to model the concentration as a log-linear function of predictors,
+    following Fisher (1993) §6.4.3-§6.4.4 (eq. 6.81, 6.82, 6.86, 6.87).
+    Per-observation SE for κ̂_i uses the delta method on (α̂, γ̂).
 
     References
     ----------
@@ -175,33 +197,53 @@ def _prepare_design(theta: Iterable[float], X: Iterable[Iterable[float]]) -> Tup
     def _parse_formula(
         self, formula: str, data: pd.DataFrame
     ) -> Tuple[np.ndarray, np.ndarray, List[str]]:
-        theta_col, x_cols = formula.split("~")
+        parts = formula.split("~")
+        if len(parts) != 2:
+            raise ValueError(
+                f"Formula must contain exactly one '~'; got: {formula!r}"
+            )
+        theta_col, x_cols = parts
         theta_series = data[theta_col.strip()]
         if theta_series.isnull().any():
             raise ValueError("Response column contains missing values.")
         theta = theta_series.to_numpy()
-        x_cols = [col.strip() for col in x_cols.split("+")]
+        x_cols = [col.strip() for col in x_cols.split("+") if col.strip()]
+        if not x_cols:
+            raise ValueError(f"No predictors found in formula: {formula!r}")
         X_df = data[x_cols]
         if X_df.isnull().any().any():
             raise ValueError("Predictor columns contain missing values.")
         X = X_df.to_numpy()
         return theta, X, x_cols
 
-    def _A1(self, kappa: np.ndarray) -> np.ndarray:
-        return i1(kappa) / i0(kappa)
-
-    def _A1inv(self, R: float) -> float:
-        if 0 <= R < 0.53:
-            return 2 * R + R**3 + (5 * R**5) / 6
-        elif R < 0.85:
-            return -0.4 + 1.39 * R + 0.43 / (1 - R)
-        else:
-            return 1 / (R**3 - 4 * R**2 + 3 * R)
-
-    def _A1_prime(self, kappa: np.ndarray) -> np.ndarray:
+    @staticmethod
+    def _A1_prime(kappa: np.ndarray) -> np.ndarray:
         a1 = A1(kappa)
         return 1 - a1 / kappa - a1**2
 
+    @staticmethod
+    def _safe_exp_kappa(eta: np.ndarray) -> np.ndarray:
+        # Bound the log-concentration to avoid exp overflow during iterations.
+        # exp(±50) ≈ {5e21, 2e-22}, comfortably finite.
+        return np.exp(np.clip(eta, -50.0, 50.0))
+
+    @staticmethod
+    def _log_i0(kappa: np.ndarray) -> np.ndarray:
+        # log I_0(κ) computed via the exponentially scaled Bessel to stay finite
+        # for large κ (raw i0 overflows around κ ≈ 710).
+        return np.asarray(kappa) + np.log(i0e(kappa))
+
+    @staticmethod
+    def _delta_se_kappa(
+        kappa: np.ndarray, X1: np.ndarray, cov_alpha_gamma: np.ndarray
+    ) -> np.ndarray:
+        # κ_i = exp(α + X_iᵀ γ); ∂κ_i/∂(α,γ) = κ_i · z_i with z_i = [1, X_i].
+        # Var(κ_i) ≈ κ_i² · z_iᵀ Σ z_i (delta method).
+        z_cov = X1 @ cov_alpha_gamma
+        quad = np.einsum("ij,ij->i", z_cov, X1)
+        var_kappa = (kappa**2) * np.clip(quad, 0.0, None)
+        return np.sqrt(var_kappa)
+
     def _fit(self):
         theta = self.theta
         n = len(theta)
@@ -211,6 +253,11 @@ def _fit(self):
         diff = self.tol + 1
         log_likelihood_old = -np.inf
 
+        # Tiny ridge added to the normal-equation LHS to keep solves finite
+        # when XtX is near-singular. Hoisted out of the loop body.
+        ridge_X = 1e-8 * np.eye(X.shape[1])
+        ridge_X1 = 1e-8 * np.eye(X1.shape[1])
+
         for iter_count in range(self.max_iter):
             if self.model_type == "mean":
                 # Step 1: Compute mu and kappa
@@ -224,80 +271,78 @@ def _fit(self):
                 # Step 2: Update beta
                 denom = 1 + (X @ beta) ** 2
                 G = 2 * X / denom[:, None]
-                weight = float(kappa * A1(np.array([kappa]))[0])
+                weight = float(kappa * A1(kappa))
                 u = kappa * np.sin(raw_deviation - mu)
                 XtX = G.T @ G
                 rhs = G.T @ u + weight * XtX @ beta
-                mat = weight * XtX + 1e-8 * np.eye(X.shape[1])
+                mat = weight * XtX + ridge_X
                 beta_new = _safe_solve(mat, rhs)
                 alpha_new, gamma_new = alpha, gamma
 
                 # Log-likelihood
-                log_likelihood = -n * np.log(i0(kappa)) + kappa * np.sum(
+                log_likelihood = -n * float(self._log_i0(kappa)) + kappa * np.sum(
                     np.cos(raw_deviation - mu)
                 )
 
             elif self.model_type == "kappa":
                 # Step 1: Compute mu and kappa
-                kappa = np.exp(alpha + X @ gamma)
+                kappa = self._safe_exp_kappa(alpha + X @ gamma)
                 S = float(np.sum(kappa * np.sin(theta)))
                 C = float(np.sum(kappa * np.cos(theta)))
                 mu = np.arctan2(S, C)
 
                 # Step 2: Update gamma
-                a1_kappa = self._A1(kappa)
-                a1_prime = self._A1_prime(kappa)
-                if np.any(np.isclose(a1_prime, 0.0)):
-                    raise ValueError("Encountered zero derivative in concentration update.")
+                a1_kappa = A1(kappa)
+                # Floor A1'(κ) to keep the IRLS step finite when some κ_i are
+                # very large (A1'(κ) → 0 as κ → ∞ ⇒ y_gamma blows up).
+                a1_prime = np.maximum(self._A1_prime(kappa), 1e-12)
                 residuals_gamma = np.cos(theta - mu) - a1_kappa
                 y_gamma = residuals_gamma / (a1_prime * kappa)
                 weights = (kappa**2) * a1_prime
                 XtWX = X1.T @ (weights[:, None] * X1)
                 XtWy = X1.T @ (weights * y_gamma)
-                update = _safe_solve(XtWX + 1e-8 * np.eye(X1.shape[1]), XtWy)
+                update = _safe_solve(XtWX + ridge_X1, XtWy)
                 alpha_new = alpha + update[0]
                 gamma_new = gamma + update[1:]
                 beta_new = beta
                 # Log-likelihood
-                log_likelihood = -np.sum(np.log(i0(kappa))) + np.sum(
+                log_likelihood = -np.sum(self._log_i0(kappa)) + np.sum(
                     kappa * np.cos(theta - mu)
                 )
 
             elif self.model_type == "mixed":
                 # Step 1: Compute mu and kappa
-                kappa = np.exp(alpha + X @ gamma)
+                kappa = self._safe_exp_kappa(alpha + X @ gamma)
                 raw_deviation = theta - 2 * np.arctan(X @ beta)
                 S = np.sum(kappa * np.sin(raw_deviation))
                 C = np.sum(kappa * np.cos(raw_deviation))
                 mu = np.arctan2(S, C)
-                residuals = theta - mu
 
-                # Step 2: Update beta
+                # Step 2: Update beta — Fisher scoring step from current β.
+                # Score s(β) = Gᵀ (κ ⊙ sin(rdev − μ)); info I(β) = Gᵀ diag(κ A1(κ)) G.
+                # β_new solves I β_new = I β + s.
                 denom = 1 + (X @ beta) ** 2
                 G = 2 * X / denom[:, None]
-                weights_beta = kappa * self._A1(kappa)
+                weights_beta = kappa * A1(kappa)
                 XtWX_beta = G.T @ (weights_beta[:, None] * G)
-                rhs_beta = G.T @ (weights_beta * np.sin(residuals))
-                beta_new = _safe_solve(
-                    XtWX_beta + 1e-8 * np.eye(X.shape[1]), rhs_beta
-                )
+                u_beta = kappa * np.sin(raw_deviation - mu)
+                rhs_beta = G.T @ u_beta + XtWX_beta @ beta
+                beta_new = _safe_solve(XtWX_beta + ridge_X, rhs_beta)
 
                 # Step 3: Update gamma
-                a1_kappa = self._A1(kappa)
-                a1_prime = self._A1_prime(kappa)
-                if np.any(np.isclose(a1_prime, 0.0)):
-                    raise ValueError("Encountered zero derivative in concentration update.")
+                a1_kappa = A1(kappa)
+                a1_prime = np.maximum(self._A1_prime(kappa), 1e-12)
                 residuals_gamma = np.cos(raw_deviation - mu) - a1_kappa
                 y_gamma = residuals_gamma / (a1_prime * kappa)
                 weights_gamma = (kappa**2) * a1_prime
                 XtWX = X1.T @ (weights_gamma[:, None] * X1)
                 XtWy = X1.T @ (weights_gamma * y_gamma)
-                update = _safe_solve(XtWX + 1e-8 * np.eye(X1.shape[1]), XtWy)
+                update = _safe_solve(XtWX + ridge_X1, XtWy)
                 alpha_new = alpha + update[0]
                 gamma_new = gamma + update[1:]
 
                 # Log-likelihood
-                log_likelihood = -np.sum(np.log(i0(kappa))) + np.sum(
+                log_likelihood = -np.sum(self._log_i0(kappa)) + np.sum(
                     kappa * np.cos(raw_deviation - mu)
                 )
 
@@ -312,6 +357,13 @@ def _fit(self):
 
             beta, alpha, gamma = beta_new, alpha_new, gamma_new
             log_likelihood_old = log_likelihood
+        else:
+            warnings.warn(
+                f"CLRegression did not converge in {self.max_iter} iterations "
+                f"(last diff={diff:.2e}, tol={self.tol:.2e}).",
+                RuntimeWarning,
+                stacklevel=2,
+            )
 
         result = {
             "beta": beta,
@@ -337,8 +389,6 @@ def _compute_standard_errors(self, result):
         n = len(theta)
         kappa = result["kappa"]
         beta = result["beta"]
-        gamma = result["gamma"]
-        alpha = result["alpha"]
 
         se_results = {}
 
@@ -346,14 +396,14 @@ def _compute_standard_errors(self, result):
             # Mean Direction Model
             denom = 1 + (X @ beta) ** 2
             G = 2 * X / denom[:, None]
-            weight = float(kappa * self._A1(np.array([kappa]))[0])
-            XtAX = weight * (G.T @ G) + 1e-8 * np.eye(X.shape[1])
+            weight = float(kappa * A1(kappa))
+            XtAX = weight * (G.T @ G)
             cov_beta = _safe_inverse(XtAX)
             se_beta = np.sqrt(np.diag(cov_beta))
 
-            denom_mu = max((n - X.shape[1]) * kappa * self._A1(np.array([kappa]))[0], 1e-12)
+            denom_mu = max((n - X.shape[1]) * kappa * A1(kappa), 1e-12)
             se_mu = 1 / np.sqrt(denom_mu)
-            denom_kappa = n * (1 - self._A1(np.array([kappa]))[0] ** 2 - self._A1(np.array([kappa]))[0] / kappa)
+            denom_kappa = n * (1 - A1(kappa) ** 2 - A1(kappa) / kappa)
             se_kappa = np.sqrt(1 / max(denom_kappa, 1e-12))
 
             se_results.update(
@@ -367,18 +417,18 @@ def _compute_standard_errors(self, result):
         elif self.model_type == "kappa":
             # Concentration Parameter Model
             X1 = np.column_stack((np.ones(n), X))  # Add intercept
-            weights = (np.exp(X1 @ np.hstack([alpha, gamma])) ** 2) * self._A1_prime(kappa)
-            XtWX = X1.T @ (weights[:, None] * X1) + 1e-8 * np.eye(X1.shape[1])
+            weights = (kappa**2) * self._A1_prime(kappa)
+            XtWX = X1.T @ (weights[:, None] * X1)
 
             cov_gamma_alpha = _safe_inverse(XtWX)
-
             se_alpha = np.sqrt(cov_gamma_alpha[0, 0])
             se_gamma = np.sqrt(np.diag(cov_gamma_alpha[1:, 1:]))
 
-            denom_mu = max(np.sum(kappa * self._A1(kappa)) - 0.5, 1e-12)
+            # Fisher (1993), eq. 6.82: σ̂_μ = (Σ κ̂_i A1(κ̂_i) − 1/2)^(−1/2).
+            denom_mu = max(float(np.sum(kappa * A1(kappa))) - 0.5, 1e-12)
             se_mu = 1 / np.sqrt(denom_mu)
 
-            se_kappa = np.sqrt(1 / np.clip(n * self._A1_prime(kappa), 1e-12, None))
+            se_kappa = self._delta_se_kappa(kappa, X1, cov_gamma_alpha)
 
             se_results.update(
                 {
@@ -393,25 +443,24 @@ def _compute_standard_errors(self, result):
             # Mixed Model
             denom = 1 + (X @ beta) ** 2
             G = 2 * X / denom[:, None]
-            weights_beta = kappa * self._A1(kappa)
-            XtGKGX = G.T @ (weights_beta[:, None] * G) + 1e-8 * np.eye(X.shape[1])
+            weights_beta = kappa * A1(kappa)
+            XtGKGX = G.T @ (weights_beta[:, None] * G)
 
             cov_beta = _safe_inverse(XtGKGX)
             se_beta = np.sqrt(np.diag(cov_beta))
 
             X1 = np.column_stack((np.ones(n), X))  # Add intercept
-            weights_gamma = (np.exp(X1 @ np.hstack([alpha, gamma])) ** 2) * self._A1_prime(kappa)
-            XtWX_gamma = X1.T @ (weights_gamma[:, None] * X1) + 1e-8 * np.eye(X1.shape[1])
+            weights_gamma = (kappa**2) * self._A1_prime(kappa)
+            XtWX_gamma = X1.T @ (weights_gamma[:, None] * X1)
 
             cov_gamma_alpha = _safe_inverse(XtWX_gamma)
             se_alpha = np.sqrt(cov_gamma_alpha[0, 0])
             se_gamma = np.sqrt(np.diag(cov_gamma_alpha[1:, 1:]))
 
-            denom_mu = max(np.sum(kappa * self._A1(kappa)) - 0.5, 1e-12)
+            # Fisher (1993), eq. 6.82: σ̂_μ = (Σ κ̂_i A1(κ̂_i) − 1/2)^(−1/2).
+            denom_mu = max(float(np.sum(kappa * A1(kappa))) - 0.5, 1e-12)
             se_mu = 1 / np.sqrt(denom_mu)
-            a1_vals = self._A1(kappa)
-            denom_kappa = n * (1 - a1_vals**2 - a1_vals / kappa)
-            se_kappa = np.sqrt(1 / np.clip(denom_kappa, 1e-12, None))
+            se_kappa = self._delta_se_kappa(kappa, X1, cov_gamma_alpha)
             se_results.update(
                 {
                     "se_beta": se_beta,
@@ -487,22 +536,182 @@ def predict(self, X_new):
         if self.result is None:
             raise ValueError("Model must be fitted before making predictions.")
 
+        X_arr = np.asarray(X_new, dtype=float)
+        if X_arr.ndim == 1:
+            X_arr = X_arr[:, None]
+        if not np.all(np.isfinite(X_arr)):
+            raise ValueError("`X_new` contains non-finite values.")
+        if X_arr.shape[1] != self.X.shape[1]:
+            raise ValueError(
+                f"Expected {self.X.shape[1]} predictors, received {X_arr.shape[1]}."
+            )
+
+        mu = self.result["mu"]
+        if self.model_type == "kappa":
+            # Conditional mean is constant μ (β is not part of the model).
+            return np.full(X_arr.shape[0], np.mod(mu, 2 * np.pi))
+
         beta = self.result.get("beta")
         if beta is None or np.any(~np.isfinite(beta)):
             raise ValueError("Model does not contain beta coefficients for prediction.")
+        return np.mod(mu + 2 * np.arctan(X_arr @ beta), 2 * np.pi)
+
+    def predict_kappa(self, X_new) -> np.ndarray:
+        """Predict per-observation concentration κ_i = exp(α + X_iᵀγ).
+
+        Only meaningful for ``model_type`` in ``{"kappa", "mixed"}``; for the
+        ``"mean"`` model the concentration is a single scalar already in
+        ``self.result["kappa"]``.
 
+        Parameters
+        ----------
+        X_new : array-like, shape (n_samples, n_features) or (n_features,)
+            New predictor data.
+
+        Returns
+        -------
+        np.ndarray, shape (n_samples,)
+        """
+        if self.model_type == "mean":
+            raise ValueError(
+                "predict_kappa() is for model_type in {'kappa', 'mixed'}; "
+                "the 'mean' model has a scalar κ in result['kappa']."
+            )
         X_arr = np.asarray(X_new, dtype=float)
         if X_arr.ndim == 1:
             X_arr = X_arr[:, None]
-        if X_arr.shape[1] != beta.size:
+        if X_arr.shape[1] != self.X.shape[1]:
             raise ValueError(
-                f"Expected {beta.size} predictors, received {X_arr.shape[1]}."
+                f"Expected {self.X.shape[1]} predictors, received {X_arr.shape[1]}."
             )
         if not np.all(np.isfinite(X_arr)):
             raise ValueError("`X_new` contains non-finite values.")
+        return self._predict_kappa(X_arr)
+
+    def _predict_kappa(self, X_arr: np.ndarray) -> np.ndarray:
+        """Internal: numpy-only κ̂(X) without input validation."""
+        alpha = self.result["alpha"]
+        gamma = self.result["gamma"]
+        eta = alpha + X_arr @ gamma
+        return np.exp(np.clip(eta, -50.0, 50.0))
+
+    def plot(
+        self,
+        figsize: Optional[Tuple[float, float]] = None,
+        n_curve: int = 200,
+        axes=None,
+    ):
+        """Two-panel diagnostic figure.
 
+        Layout depends on ``model_type`` and the number of predictors:
+
+        - 1D X, ``model_type`` in ``{"mean", "mixed"}``: fit overlay
+          (data and curve replicated at θ and θ+2π) and residuals vs X.
+        - 1D X, ``model_type`` == ``"kappa"``: data scatter with the
+          constant μ line, plus fitted κ_i = exp(α + X_iᵀγ) on the right.
+        - Multi-D X: residuals vs fitted angle, plus residual histogram.
+
+        Returns
+        -------
+        matplotlib.figure.Figure
+        """
+        import matplotlib.pyplot as plt
+
+        n_features = self.X.shape[1]
+        is_1d = n_features == 1
+
+        if axes is None:
+            fig, axes = plt.subplots(1, 2, figsize=figsize or (11, 5))
+        else:
+            axes = list(axes)
+            if len(axes) != 2:
+                raise ValueError("`axes` must be a sequence of length 2.")
+            fig = axes[0].figure
+
+        if not is_1d:
+            self._plot_residual_diagnostic(axes)
+            fig.tight_layout()
+            return fig
+
+        x_data = self.X[:, 0]
+        theta_data = np.mod(self.theta, 2 * np.pi)
+        feature_label = self.feature_names[0]
+
+        x_grid = np.linspace(x_data.min(), x_data.max(), n_curve)
+        x_grid_2d = x_grid[:, None]
+
+        ax = axes[0]
+        if self.model_type in ("mean", "mixed"):
+            mu = self.result["mu"]
+            beta = self.result["beta"]
+            curve = np.mod(mu + 2 * np.arctan(x_grid * beta[0]), 2 * np.pi)
+            curve_plot = curve.astype(float).copy()
+            jumps = np.where(np.abs(np.diff(curve)) > np.pi)[0]
+            curve_plot[jumps] = np.nan
+            ax.plot(x_grid, curve_plot, color="C1", lw=2, label="fit")
+            ax.plot(x_grid, curve_plot + 2 * np.pi, color="C1", lw=2)
+        else:  # kappa-only: conditional mean is the constant μ.
+            mu = self.result["mu"]
+            ax.axhline(mu, color="C1", lw=2, label=f"μ = {mu:.3f}")
+            ax.axhline(mu + 2 * np.pi, color="C1", lw=2)
+
+        ax.scatter(x_data, theta_data, color="C0", s=20, alpha=0.6, edgecolors="none", label="data")
+        ax.scatter(x_data, theta_data + 2 * np.pi, color="C0", s=20, alpha=0.6, edgecolors="none")
+        ax.set_ylim(0, 4 * np.pi)
+        ax.set_yticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi])
+        ax.set_yticklabels(["0", "π", "2π", "3π", "4π"])
+        ax.set_xlabel(feature_label)
+        ax.set_ylabel("θ")
+        ax.set_title("Fit overlay")
+        ax.legend(loc="best", frameon=False)
+
+        ax = axes[1]
+        if self.model_type == "kappa":
+            kappa_curve = self._predict_kappa(x_grid_2d)
+            ax.plot(x_grid, kappa_curve, color="C1", lw=2)
+            ax.set_ylabel("κ̂(X) = exp(α + Xγ)")
+            ax.set_title("Fitted concentration")
+        else:
+            residuals = np.angle(np.exp(1j * (self.theta - self._fitted_mean())))
+            ax.scatter(x_data, residuals, color="C0", s=20, alpha=0.6, edgecolors="none")
+            ax.axhline(0.0, color="k", lw=0.5)
+            ax.set_ylabel("Residual (rad)")
+            ax.set_title("Residuals vs X")
+        ax.set_xlabel(feature_label)
+
+        fig.tight_layout()
+        return fig
+
+    def _fitted_mean(self) -> np.ndarray:
+        """Conditional mean angle at the training X (constant μ for kappa-only)."""
         mu = self.result["mu"]
-        return mu + 2 * np.arctan(X_arr @ beta)
+        if self.model_type == "kappa":
+            return np.full(self.theta.shape, mu)
+        beta = self.result["beta"]
+        return mu + 2 * np.arctan(self.X @ beta)
+
+    def _plot_residual_diagnostic(self, axes) -> None:
+        residuals = np.angle(np.exp(1j * (self.theta - self._fitted_mean())))
+        fitted = np.mod(self._fitted_mean(), 2 * np.pi)
+        ax = axes[0]
+        ax.scatter(fitted, residuals, color="C0", s=20, alpha=0.6, edgecolors="none")
+        ax.axhline(0.0, color="k", lw=0.5)
+        ax.set_xlabel("Fitted θ (rad)")
+        ax.set_ylabel("Residual (rad)")
+        ax.set_title("Residuals vs fitted")
+
+        ax = axes[1]
+        ax.hist(residuals, bins=20, color="C0", alpha=0.7, edgecolor="black")
+        ax.axvline(0.0, color="k", lw=0.5)
+        ax.set_xlabel("Residual (rad)")
+        ax.set_ylabel("Count")
+        ax.set_title("Residual histogram")
+
+    @staticmethod
+    def _two_sided_p(t_value: float) -> float:
+        if np.isnan(t_value):
+            return np.nan
+        return float(2.0 * norm.sf(np.abs(t_value)))
 
     def summary(self):
         if self.result is None:
@@ -533,12 +742,8 @@ def summary(self):
             )
             for i, coef in enumerate(self.result["beta"]):
                 se_val = se_beta[i]
-                t_value = abs(coef / se_val) if se_val else np.nan
-                p_value = (
-                    2 * (1 - norm.cdf(np.abs(t_value)))
-                    if not np.isnan(t_value)
-                    else np.nan
-                )
+                t_value = coef / se_val if se_val else np.nan
+                p_value = self._two_sided_p(t_value)
                 print(
                     f"β{i:<3} {coef:<12.5f} {se_val:<12.5f} {t_value:<10.2f} {p_value:<12.5f}{significance_code(p_value):<3}"
                 )
@@ -559,22 +764,14 @@ def summary(self):
             # Report alpha as the first coefficient
             alpha = self.result["alpha"]
             t_value_alpha = alpha / se_alpha if se_alpha else np.nan
-            p_value_alpha = (
-                2 * (1 - norm.cdf(np.abs(t_value_alpha)))
-                if not np.isnan(t_value_alpha)
-                else np.nan
-            )
+            p_value_alpha = self._two_sided_p(t_value_alpha)
             print(
                 f"α{'':<5} {alpha:<12.5f} {se_alpha:<12.5f} {t_value_alpha:<10.2f} {p_value_alpha:<12.5f}{significance_code(p_value_alpha)}"
             )
             for i, coef in enumerate(self.result["gamma"]):
                 se_val = se_gamma[i]
                 t_value = coef / se_val if se_val else np.nan
-                p_value = (
-                    2 * (1 - norm.cdf(np.abs(t_value)))
-                    if not np.isnan(t_value)
-                    else np.nan
-                )
+                p_value = self._two_sided_p(t_value)
                 print(
                     f"γ{i:<5} {coef:<12.5f} {se_val:<12.5f} {t_value:<10.2f} {p_value:<12.5f}{significance_code(p_value)}"
                 )
@@ -597,10 +794,9 @@ def summary(self):
             for i, k in enumerate(kappa, start=1):
                 se_val = se_kappa[i - 1] if se_kappa is not None else float("nan")
                 print(f"    [{i}]    {k:>10.5f}    {se_val:>10.5f}")
-            if se_kappa is not None:
-                print(f"    Mean:    {np.mean(kappa):.5f} (SE: {np.mean(se_kappa):.5f})")
-            else:
-                print(f"    Mean:    {np.mean(kappa):.5f}")
+            # Per-obs κ_i are correlated (shared α, γ), so averaging individual
+            # SEs is not the SE of the mean — report only the point estimate.
+            print(f"    Mean:    {np.mean(kappa):.5f}")
         else:
             if se_kappa is not None:
                 print(f"    κ: {kappa:.5f} (SE: {se_kappa:.5f})")
@@ -650,18 +846,28 @@ class CCRegression:
         Coefficients of the cos and sin terms for each harmonic order.
     p_values : np.ndarray
         P-values for higher-order terms.
+    kappa : float
+        Concentration of the residuals, A1⁻¹(mean cos(residuals)).
+    A_k : float
+        Mean cosine of the residuals (input to A1⁻¹).
     message : str
         Message indicating the significance of higher-order terms.
 
     Methods
     -------
     summary()
-        Print a summary of the regression results.
-
+        Print the harmonic coefficient table, ρ, residual κ, and the test
+        of higher-order terms.
+    predict(x)
+        Predict the circular response at new ``x``.
+    plot(figsize=None, n_curve=200, axes=None)
+        Two-panel diagnostic figure (fit overlay for 1-D ``x``; residuals
+        vs fitted + histogram for multi-D).
 
     Notes
     -----
-    The implementation is ported from the `lm.circular.cc` in the `circular` R package.
+    The implementation is ported from the ``lm.circular.cc`` in the
+    ``circular`` R package (Agostinelli & Lund).
 
     References
     ----------
@@ -693,6 +899,13 @@ def __init__(
         else:
             raise ValueError("Provide either a formula + data or theta and x.")
 
+        if self.theta.ndim != 1:
+            raise ValueError(
+                f"`theta` must be 1-dimensional (got shape {self.theta.shape})."
+            )
+        if self.theta.size != self.x.shape[0]:
+            raise ValueError("`theta` and `x` must have matching numbers of rows.")
+
         self.order = order
         self.level = level
 
@@ -701,54 +914,84 @@ def __init__(
         if not (0 < self.level < 1):
             raise ValueError("`level` must lie between 0 and 1.")
 
+        n_params = 1 + 2 * self.x.shape[1] * self.order
+        if self.theta.size <= n_params:
+            raise ValueError(
+                f"order={self.order} requires more than {n_params} observations "
+                f"(got {self.theta.size}); reduce `order` or provide more data."
+            )
+
         # Fit the model
         self.result = self._fit()
 
     @staticmethod
     def _validate_input(arr: np.ndarray) -> np.ndarray:
-        """
-        Validate input array and ensure it is in radians.
+        """Validate angular input and wrap to ``[0, 2π)``.
+
+        The model is 2π-periodic, so values are normalised modulo ``2π``.
+        Input is expected to be in radians; degrees would silently wrap to
+        the wrong range (e.g. 360° → 360 mod 2π ≈ 5.97 rad ≈ 342°).
         """
         arr_np = np.asarray(arr, dtype=float)
         if arr_np.ndim == 0:
             raise ValueError("Input must be at least one-dimensional.")
         if not np.all(np.isfinite(arr_np)):
             raise ValueError("Circular input contains non-finite values.")
+        if arr_np.size and float(np.max(np.abs(arr_np))) > 4 * np.pi:
+            warnings.warn(
+                "Circular input contains values with |x| > 4π; expected "
+                "radians. Degree-valued input will be silently wrapped "
+                "modulo 2π and produce incorrect results — use np.deg2rad.",
+                UserWarning,
+                stacklevel=3,
+            )
         return np.mod(arr_np, 2 * np.pi)
 
     def _parse_formula(
         self, formula: str, data: pd.DataFrame
     ) -> Tuple[np.ndarray, np.ndarray, List[str]]:
-        theta_col, x_cols = formula.split("~")
+        parts = formula.split("~")
+        if len(parts) != 2:
+            raise ValueError(
+                f"Formula must contain exactly one '~'; got: {formula!r}"
+            )
+        theta_col, x_cols = parts
         theta = data[theta_col.strip()].to_numpy()
-        x_cols = [col.strip() for col in x_cols.split("+")]
+        x_cols = [col.strip() for col in x_cols.split("+") if col.strip()]
+        if not x_cols:
+            raise ValueError(f"No predictors found in formula: {formula!r}")
         X = data[x_cols].to_numpy()
         return theta, X, x_cols
 
+    def _design_matrix(self, x: np.ndarray) -> np.ndarray:
+        """Harmonic design matrix [1 | cos(kx_j) | sin(kx_j)] for given x."""
+        if x.ndim == 1:
+            x = x[:, None]
+        n, n_features = x.shape
+        cos_terms, sin_terms = [], []
+        for j in range(n_features):
+            for k in range(1, self.order + 1):
+                cos_terms.append(np.cos(k * x[:, j]))
+                sin_terms.append(np.sin(k * x[:, j]))
+        return np.column_stack([np.ones(n)] + cos_terms + sin_terms)
+
     def _fit(self):
         n = self.x.shape[0]
         order = self.order
         n_features = self.x.shape[1]
 
-        # Create harmonic terms
-        cos_terms = []
-        sin_terms = []
+        # Track which (feature, harmonic) each design column corresponds to.
         cos_labels: List[Tuple[int, int]] = []
         sin_labels: List[Tuple[int, int]] = []
         for j in range(n_features):
-            x_col = self.x[:, j]
             for k in range(1, order + 1):
-                cos_terms.append(np.cos(k * x_col))
-                sin_terms.append(np.sin(k * x_col))
                 cos_labels.append((j, k))
                 sin_labels.append((j, k))
 
-        # Linear models for cos(theta) and sin(theta)
         Y_cos = np.cos(self.theta)
         Y_sin = np.sin(self.theta)
 
-        design_matrix = [np.ones(n)] + cos_terms + sin_terms
-        X = np.column_stack(design_matrix)
+        X = self._design_matrix(self.x)
         beta_cos, _, _, _ = lstsq(X, Y_cos)
         beta_sin, _, _, _ = lstsq(X, Y_sin)
 
@@ -757,12 +1000,25 @@ def _fit(self):
         sin_fit = X @ beta_sin
         fitted = np.mod(np.arctan2(sin_fit, cos_fit), 2 * np.pi)
 
-        # Residuals
+        # Residuals (angular for diagnostics + raw OLS residuals on cos/sin)
         residuals = np.angle(np.exp(1j * (self.theta - fitted)))
+        residual_cos = Y_cos - cos_fit
+        residual_sin = Y_sin - sin_fit
 
         # Circular correlation coefficient
         rho = float(np.clip(np.sqrt(np.mean(cos_fit**2 + sin_fit**2)), 0.0, 1.0))
 
+        # Per-coefficient OLS SEs for the cos/sin sub-models. Used by summary()
+        # to print a CL/LC-style coefficient table; not part of the R parity
+        # surface (lm.circular.cc returns only the coefficient matrix).
+        XtX_inv = _safe_inverse(X.T @ X)
+        diag_inv = np.maximum(np.diag(XtX_inv), 0.0)
+        df_resid = max(n - X.shape[1], 1)
+        sigma2_cos = float(residual_cos @ residual_cos) / df_resid
+        sigma2_sin = float(residual_sin @ residual_sin) / df_resid
+        se_beta_cos = np.sqrt(sigma2_cos * diag_inv)
+        se_beta_sin = np.sqrt(sigma2_sin * diag_inv)
+
         # Test higher-order terms
         higher_order_cos = []
         higher_order_sin = []
@@ -777,26 +1033,22 @@ def _fit(self):
 
         # Projection matrix for the current model
         if W.size:
-            XtX = X.T @ X
-            M = X @ _safe_inverse(XtX) @ X.T
+            M = X @ XtX_inv @ X.T
             H = W.T @ (np.eye(n) - M) @ W
             H_inv = _safe_inverse(H)
             N = W @ H_inv @ W.T
 
-            residual_cos = Y_cos - X @ beta_cos
-            residual_sin = Y_sin - X @ beta_sin
-
-            denom_cos = float(residual_cos.T @ residual_cos)
-            denom_sin = float(residual_sin.T @ residual_sin)
+            denom_cos = float(residual_cos @ residual_cos)
+            denom_sin = float(residual_sin @ residual_sin)
             adj = max(n - (2 * order + 1), 1)
             T1 = (
                 adj
-                * float(residual_cos.T @ N @ residual_cos)
+                * float(residual_cos @ N @ residual_cos)
                 / max(denom_cos, 1e-12)
             )
             T2 = (
                 adj
-                * float(residual_sin.T @ N @ residual_sin)
+                * float(residual_sin @ N @ residual_sin)
                 / max(denom_sin, 1e-12)
             )
 
@@ -816,6 +1068,19 @@ def _fit(self):
         else:
             message = f"Higher-order terms are significant at the {self.level} level."
 
+        # Residual concentration (R parity): A1inv of the mean cosine of residuals.
+        A_k = float(np.mean(np.cos(residuals)))
+        if A_k < 0:
+            warnings.warn(
+                f"Mean residual cosine A_k={A_k:.4f} is negative — residuals "
+                "are systematically anti-aligned with the fitted direction. "
+                "κ has been clamped to 0; check for sign errors or model "
+                "misspecification.",
+                UserWarning,
+                stacklevel=3,
+            )
+        kappa_residual = float(A1inv(A_k))
+
         return {
             "rho": rho,
             "fitted": fitted,
@@ -824,50 +1089,613 @@ def _fit(self):
                 "cos": beta_cos,
                 "sin": beta_sin,
             },
+            "se_coefficients": {
+                "cos": se_beta_cos,
+                "sin": se_beta_sin,
+            },
+            "df_resid": df_resid,
             "cos_labels": cos_labels,
             "sin_labels": sin_labels,
             "p_values": p_values,
+            "A_k": A_k,
+            "kappa": kappa_residual,
             "message": message,
         }
 
+    def predict(self, x: np.ndarray) -> np.ndarray:
+        """Predict the circular response at new predictor values.
+
+        Parameters
+        ----------
+        x : array-like, shape (n,) or (n, n_features)
+            New predictor values in radians. For multi-feature models the
+            second axis must match ``self.x.shape[1]``.
+
+        Returns
+        -------
+        np.ndarray, shape (n,)
+            Predicted angles wrapped to ``[0, 2π)``.
+        """
+        x_arr = np.asarray(x, dtype=float)
+        if x_arr.ndim == 1:
+            x_arr = x_arr[:, None]
+        if x_arr.shape[1] != self.x.shape[1]:
+            raise ValueError(
+                f"Expected {self.x.shape[1]} predictor column(s); received "
+                f"{x_arr.shape[1]}."
+            )
+        x_arr = np.mod(x_arr, 2 * np.pi)
+        design = self._design_matrix(x_arr)
+        cos_pred = design @ self.result["coefficients"]["cos"]
+        sin_pred = design @ self.result["coefficients"]["sin"]
+        return np.mod(np.arctan2(sin_pred, cos_pred), 2 * np.pi)
+
+    def plot(
+        self,
+        figsize: Optional[Tuple[float, float]] = None,
+        n_curve: int = 200,
+        axes=None,
+    ):
+        """Two-panel diagnostic figure.
+
+        For a single circular predictor, the left panel is a fit overlay
+        with both data and curve replicated at ``θ`` and ``θ + 2π`` (Pewsey
+        Fig 6.10 convention) so the wrap-around does not visually break the
+        relationship; the right panel shows the wrapped residuals against
+        the predictor.
+
+        For multiple circular predictors, the left panel shows residuals
+        vs the fitted angle and the right panel a residual histogram.
+
+        Returns
+        -------
+        matplotlib.figure.Figure
+        """
+        import matplotlib.pyplot as plt
+
+        n_features = self.x.shape[1]
+
+        if axes is None:
+            fig, axes = plt.subplots(1, 2, figsize=figsize or (11, 5))
+        else:
+            axes = list(axes)
+            if len(axes) != 2:
+                raise ValueError("`axes` must be a sequence of length 2.")
+            fig = axes[0].figure
+
+        if n_features == 1:
+            x_data = self.x[:, 0]
+            theta_data = self.theta
+            residuals = self.result["residuals"]
+            x_grid = np.linspace(0.0, 2 * np.pi, n_curve)
+            theta_pred = self.predict(x_grid)
+            # Break the curve where it wraps so plot() doesn't draw a
+            # vertical jump connecting 2π to 0.
+            theta_plot = theta_pred.astype(float).copy()
+            jumps = np.where(np.abs(np.diff(theta_pred)) > np.pi)[0]
+            theta_plot[jumps] = np.nan
+
+            ax = axes[0]
+            ax.plot(x_grid, theta_plot, color="C1", lw=2, label="fit")
+            ax.plot(x_grid, theta_plot + 2 * np.pi, color="C1", lw=2)
+            ax.scatter(x_data, theta_data, color="C0", s=20, alpha=0.6, edgecolors="none", label="data")
+            ax.scatter(x_data, theta_data + 2 * np.pi, color="C0", s=20, alpha=0.6, edgecolors="none")
+            ax.set_xlim(0, 2 * np.pi)
+            ax.set_ylim(0, 4 * np.pi)
+            ax.set_xticks([0, np.pi / 2, np.pi, 3 * np.pi / 2, 2 * np.pi])
+            ax.set_xticklabels(["0", "π/2", "π", "3π/2", "2π"])
+            ax.set_yticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi])
+            ax.set_yticklabels(["0", "π", "2π", "3π", "4π"])
+            ax.set_xlabel(self.feature_names[0])
+            ax.set_ylabel("θ")
+            ax.set_title("Fit overlay")
+            ax.legend(loc="best", frameon=False)
+
+            ax = axes[1]
+            ax.scatter(x_data, residuals, color="C0", s=20, alpha=0.6, edgecolors="none")
+            ax.axhline(0.0, color="k", lw=0.5)
+            ax.set_xlim(0, 2 * np.pi)
+            ax.set_xticks([0, np.pi / 2, np.pi, 3 * np.pi / 2, 2 * np.pi])
+            ax.set_xticklabels(["0", "π/2", "π", "3π/2", "2π"])
+            ax.set_xlabel(self.feature_names[0])
+            ax.set_ylabel("Residual (rad)")
+            ax.set_title("Residuals vs predictor")
+        else:
+            residuals = self.result["residuals"]
+            fitted = self.result["fitted"]
+
+            ax = axes[0]
+            ax.scatter(fitted, residuals, color="C0", s=20, alpha=0.6, edgecolors="none")
+            ax.axhline(0.0, color="k", lw=0.5)
+            ax.set_xlabel("Fitted θ (rad)")
+            ax.set_ylabel("Residual (rad)")
+            ax.set_title("Residuals vs fitted")
+
+            ax = axes[1]
+            ax.hist(residuals, bins=20, color="C0", alpha=0.7, edgecolor="black")
+            ax.axvline(0.0, color="k", lw=0.5)
+            ax.set_xlabel("Residual (rad)")
+            ax.set_ylabel("Count")
+            ax.set_title("Residual histogram")
+
+        fig.tight_layout()
+        return fig
+
     def summary(self):
         """
         Print a summary of the regression results.
         """
         print("\nCircular-Circular Regression\n")
-        print(f"Circular Correlation Coefficient (rho): {self.result['rho']:.5f}\n")
+        print(f"Circular Correlation Coefficient (rho): {self.result['rho']:.5f}")
+        print(f"Mean Residual Cosine (A_k):             {self.result['A_k']:.5f}")
+        print(f"Residual Concentration (kappa):         {self.result['kappa']:.5f}\n")
 
-        print("Coefficients:")
         cos_coeffs = self.result["coefficients"]["cos"]
         sin_coeffs = self.result["coefficients"]["sin"]
+        se_cos = self.result["se_coefficients"]["cos"]
+        se_sin = self.result["se_coefficients"]["sin"]
+        df_resid = self.result["df_resid"]
         cos_labels = self.result.get("cos_labels", [])
         sin_labels = self.result.get("sin_labels", [])
 
-        # Headers
-        print(f"{'Harmonic':<12} {'Cosine Coeff':<14} {'Sine Coeff':<14}")
-
-        # Intercept
-        print(f"{'(Intercept)':<12} {cos_coeffs[0]:<14.5f} {sin_coeffs[0]:<14.5f}")
+        intercept_label = "(Intercept)"
+        cos_label_strs = [f"cos(x{f + 1},k={k})" for (f, k) in cos_labels]
+        sin_label_strs = [f"sin(x{f + 1},k={k})" for (f, k) in sin_labels]
+        row_labels = [intercept_label, *cos_label_strs, *sin_label_strs]
+        label_width = max(12, *(len(s) for s in row_labels))
 
-        # Cosine harmonics
-        offset = 1
-        for idx, (feature_idx, harmonic) in enumerate(cos_labels):
-            label = f"cos(x{feature_idx + 1},k={harmonic})"
+        def _print_block(title: str, coefs: np.ndarray, ses: np.ndarray) -> None:
+            print(f"{title}:\n")
             print(
-                f"{label:<12} {cos_coeffs[offset + idx]:<14.5f} {sin_coeffs[offset + idx]:<14.5f}"
+                f"{'':<{label_width}} {'Estimate':<12} {'Std. Error':<12} "
+                f"{'t value':<10} {'Pr(>|t|)':<12}"
             )
+            for label, coef, se_val in zip(row_labels, coefs, ses):
+                t_val = coef / se_val if se_val else np.nan
+                if np.isnan(t_val):
+                    p_val = np.nan
+                else:
+                    p_val = float(2.0 * student_t.sf(np.abs(t_val), df=df_resid))
+                print(
+                    f"{label:<{label_width}} {coef:<12.5f} {se_val:<12.5f} "
+                    f"{t_val:<10.2f} {p_val:<12.5f}{significance_code(p_val)}"
+                )
+            print()
 
-        # Sine harmonics
-        sine_offset = offset + len(cos_labels)
-        for idx, (feature_idx, harmonic) in enumerate(sin_labels):
-            label = f"sin(x{feature_idx + 1},k={harmonic})"
-            print(
-                f"{label:<12} {cos_coeffs[sine_offset + idx]:<14.5f} {sin_coeffs[sine_offset + idx]:<14.5f}"
+        _print_block("Coefficients (Cosine Model)", cos_coeffs, se_cos)
+        _print_block("Coefficients (Sine Model)", sin_coeffs, se_sin)
+
+        # Higher-order test (parity with R's lm.circular.cc): jointly tests
+        # whether the order+1 cos/sin pair adds explanatory power, separately
+        # for the cosine and sine sub-models.
+        p1, p2 = self.result["p_values"]
+        print("Higher-Order Terms Test:\n")
+        print(f"{'':<{label_width}} {'Pr(>χ²)':<12}")
+        print(f"{'cosine model':<{label_width}} {p1:<12.5f}{significance_code(p1)}")
+        print(f"{'sine model':<{label_width}} {p2:<12.5f}{significance_code(p2)}")
+
+        print(f"\n{self.result['message']}")
+        print(
+            "\nSignif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1"
+        )
+        print(
+            "Per-coefficient p-values use the t distribution; the higher-order "
+            "test uses χ² (Jammalamadaka & Sengupta 2001).\n"
+        )
+
+
+# Markers used by LCRegression's formula parser.
+# `[^\W\d_]\w*` matches a Python-style identifier including Unicode letters
+# (e.g. Greek `θ`), while still forbidding a leading digit.
+_LC_IDENT = r"[^\W\d_]\w*"
+# Accept both `harmonic(theta, k=K)` and `harmonic(theta, K)`.
+_LC_HARMONIC_RE = re.compile(
+    rf"harmonic\s*\(\s*({_LC_IDENT})\s*(?:,\s*(?:k\s*=\s*)?(\d+)\s*)?\)"
+)
+_LC_UNSUPPORTED_RE = re.compile(r"\b(skew|flat)\s*\(")
+# Coefficient-name pattern as emitted by hea: e.g. "cos(theta)",
+# "sin(2 * theta)", or "cos(theta * 2)" — multiplier may appear on either side.
+_LC_TRIG_RE = re.compile(
+    rf"^(cos|sin)\(\s*"
+    rf"(?:(?P<lmult>\d+)\s*\*\s*(?P<lvar>{_LC_IDENT})"
+    rf"|(?P<rvar>{_LC_IDENT})(?:\s*\*\s*(?P<rmult>\d+))?)"
+    rf"\s*\)$"
+)
+
+
+class LCRegression:
+    """
+    Linear–Circular Regression.
+
+    Models a linear response Y as a function of a circular regressor θ
+    (in radians). Backed by ``hea.lm``.
+
+    Formula syntax
+    --------------
+    The right-hand side accepts either a marker that expands to a Fourier
+    basis, or fully explicit ``cos(...) / sin(...)`` terms (or both).
+
+    - ``"y ~ harmonic(theta)"`` — basic cosine model (Pewsey et al. 2014, §8.4.1)
+    - ``"y ~ harmonic(theta, k=K)"`` — extended model with K harmonics (§8.4.2)
+    - ``"y ~ cos(theta) + sin(theta) + cos(3*theta) + sin(3*theta)"`` —
+      fully explicit; useful for non-contiguous harmonic orders
+    - ``"y ~ harmonic(theta, k=2) + temperature"`` — mix marker with extra
+      linear covariates
+
+    Markers ``skew(theta)`` and ``flat(theta)`` are reserved for the
+    nonlinear models in §8.4.3 / §8.4.4 and currently raise
+    ``NotImplementedError`` (they need a nonlinear least-squares backend
+    that ``hea`` does not yet provide).
+
+    Parameters
+    ----------
+    formula : str
+        R-style formula. See above.
+    data : pandas.DataFrame or polars.DataFrame
+        Input data. Pandas inputs are converted to polars internally.
+
+    Attributes
+    ----------
+    formula : str
+        The original formula passed in.
+    expanded_formula : str
+        Formula after marker expansion, as actually fit by ``hea.lm``.
+    lm_fit : hea.lm
+        The underlying linear-model fit. Use it for diagnostics
+        (``.plot()``, ``.summary()``, ``.r_squared``, etc.).
+    result : dict
+        - coefficients : dict of {name: value} from the linear fit
+        - harmonics : list of dicts, one per matched ``cos(k·θ)/sin(k·θ)``
+          pair, each with ``variable``, ``k``, ``cos_coef``, ``sin_coef``,
+          ``amplitude``, ``phase``, ``se_amplitude``, ``se_phase`` (the
+          last two via the delta method on the (cos, sin) covariance).
+        - sigma, r_squared, aic, bic : scalars
+        - fitted, residuals : np.ndarray
+
+    Notes
+    -----
+    The harmonic-pair detector recognises only **integer** multipliers,
+    written on either side of ``*`` (e.g. ``cos(theta)``, ``cos(2*theta)``,
+    ``cos(theta*2)``). A term like ``cos(0.5*theta)`` is treated as a regular
+    linear predictor and won't appear in ``result['harmonics']``.
+
+    References
+    ----------
+    Pewsey, A., Neuhäuser, M., Ruxton, G. D. (2014). *Circular Statistics
+    in R*. Oxford University Press, §8.4.
+    """
+
+    def __init__(
+        self,
+        formula: str,
+        data: Union[pd.DataFrame, "pl.DataFrame"],
+    ):
+        if not isinstance(formula, str) or "~" not in formula:
+            raise ValueError(
+                f"Formula must be a string containing '~'; got {formula!r}"
             )
 
-        print("\nP-values for Higher-Order Terms:")
+        self.formula = formula
+        self.response = formula.split("~", 1)[0].strip()
+        self.data = self._to_polars(data)
+        self.expanded_formula = self._expand_formula(formula)
+        self.lm_fit = _hea_lm(self.expanded_formula, self.data)
+        self.result = self._build_result()
+
+    @staticmethod
+    def _to_polars(data: Union[pd.DataFrame, "pl.DataFrame"]) -> "pl.DataFrame":
+        if isinstance(data, pl.DataFrame):
+            return data
+        if isinstance(data, pd.DataFrame):
+            return pl.from_pandas(data)
+        raise TypeError(
+            f"`data` must be a pandas or polars DataFrame; got {type(data).__name__}"
+        )
+
+    @staticmethod
+    def _expand_formula(formula: str) -> str:
+        lhs, _, rhs = formula.partition("~")
+        if _LC_UNSUPPORTED_RE.search(rhs):
+            raise NotImplementedError(
+                "skew() and flat() markers require a nonlinear least-squares "
+                "backend (hea.nls), which is not yet available."
+            )
+
+        def _expand(match: "re.Match[str]") -> str:
+            col = match.group(1)
+            k = int(match.group(2)) if match.group(2) else 1
+            if k < 1:
+                raise ValueError(f"harmonic(..., k={k}): k must be a positive integer.")
+            terms = []
+            for j in range(1, k + 1):
+                if j == 1:
+                    terms.append(f"cos({col}) + sin({col})")
+                else:
+                    terms.append(f"cos({j}*{col}) + sin({j}*{col})")
+            return " + ".join(terms)
+
+        expanded_rhs = _LC_HARMONIC_RE.sub(_expand, rhs)
+        return f"{lhs.strip()} ~ {expanded_rhs.strip()}"
+
+    def _build_result(self) -> dict:
+        bhat_df = self.lm_fit.bhat
+        coef_names = list(bhat_df.columns)
+        coef_values = list(bhat_df.row(0))
+        coefficients = dict(zip(coef_names, coef_values))
+
+        cov = np.asarray(self.lm_fit.V_bhat, dtype=float)
+        column_names = list(self.lm_fit.column_names)
+        name_to_idx = {n: i for i, n in enumerate(column_names)}
+
+        # Group cos/sin terms by (variable, multiplier).
+        groups: dict = {}
+        for name, value in coefficients.items():
+            m = _LC_TRIG_RE.match(name)
+            if not m:
+                continue
+            func = m.group(1)
+            if m.group("lvar") is not None:
+                var = m.group("lvar")
+                k = int(m.group("lmult"))
+            else:
+                var = m.group("rvar")
+                k = int(m.group("rmult")) if m.group("rmult") else 1
+            slot = groups.setdefault((var, k), {})
+            slot[func] = (name, value)
+
+        harmonics = []
+        for (var, k), pair in sorted(groups.items(), key=lambda kv: (kv[0][0], kv[0][1])):
+            cos_entry = pair.get("cos")
+            sin_entry = pair.get("sin")
+            cos_val = cos_entry[1] if cos_entry else None
+            sin_val = sin_entry[1] if sin_entry else None
+
+            amplitude = phase = se_amp = se_phase = None
+            if cos_val is not None and sin_val is not None:
+                amplitude = float(np.hypot(cos_val, sin_val))
+                phase = float(np.arctan2(sin_val, cos_val))
+                # Delta-method SEs from the (c, s) covariance block.
+                ic = name_to_idx.get(cos_entry[0])
+                isn = name_to_idx.get(sin_entry[0])
+                if ic is not None and isn is not None and amplitude > 0:
+                    var_c = cov[ic, ic]
+                    var_s = cov[isn, isn]
+                    cov_cs = cov[ic, isn]
+                    r2 = amplitude ** 2
+                    var_amp = (
+                        cos_val ** 2 * var_c
+                        + 2 * cos_val * sin_val * cov_cs
+                        + sin_val ** 2 * var_s
+                    ) / r2
+                    var_phase = (
+                        sin_val ** 2 * var_c
+                        - 2 * cos_val * sin_val * cov_cs
+                        + cos_val ** 2 * var_s
+                    ) / (r2 ** 2)
+                    se_amp = float(np.sqrt(max(var_amp, 0.0)))
+                    se_phase = float(np.sqrt(max(var_phase, 0.0)))
+
+            harmonics.append(
+                {
+                    "variable": var,
+                    "k": k,
+                    "cos_coef": cos_val,
+                    "sin_coef": sin_val,
+                    "amplitude": amplitude,
+                    "phase": phase,
+                    "se_amplitude": se_amp,
+                    "se_phase": se_phase,
+                }
+            )
+
+        residuals = self.lm_fit.residuals
+        if isinstance(residuals, pl.DataFrame):
+            residuals = residuals.to_numpy().ravel()
+        fitted = self.lm_fit.yhat
+        if isinstance(fitted, pl.DataFrame):
+            fitted = fitted.to_numpy().ravel()
+
+        return {
+            "coefficients": coefficients,
+            "harmonics": harmonics,
+            "sigma": float(self.lm_fit.sigma),
+            "r_squared": float(self.lm_fit.r_squared),
+            "aic": float(self.lm_fit.AIC),
+            "bic": float(self.lm_fit.BIC),
+            "fitted": np.asarray(fitted, dtype=float),
+            "residuals": np.asarray(residuals, dtype=float),
+        }
+
+    def predict(
+        self, data: Union[pd.DataFrame, "pl.DataFrame"]
+    ) -> np.ndarray:
+        """Predict the linear response for new values of the regressors."""
+        new = self._to_polars(data)
+        out = self.lm_fit.predict(new=new)
+        if isinstance(out, pl.DataFrame):
+            out = out.to_numpy().ravel()
+        return np.asarray(out, dtype=float)
+
+    def summary(self) -> None:
+        """Print a full diagnostic summary.
+
+        Reuses ``hea.lm.summary()`` for the standard regression block
+        (residual quantiles, coefficient table with SEs/CIs/t/p, fit metrics)
+        and appends a harmonic-decomposition table with delta-method SEs and
+        95% CIs for each cos/sin amplitude and phase.
+        """
+        print("\nLinear-Circular Regression")
+        if self.expanded_formula != self.formula:
+            print(f"User formula:     {self.formula}")
+            print(f"Expanded formula: {self.expanded_formula}")
+        print()
+
+        # hea.lm.summary() prints to stdout and returns None.
+        self.lm_fit.summary()
+
+        if self.result["harmonics"]:
+            self._print_harmonic_table()
+
+    def _print_harmonic_table(self) -> None:
+        z = float(norm.ppf(0.975))
+
+        def _fmt(value):
+            return "n/a" if value is None else f"{value:.4f}"
+
+        def _ci(value, se):
+            if value is None or se is None:
+                return "n/a"
+            return f"[{value - z * se:.4f}, {value + z * se:.4f}]"
+
+        rows = []
+        for h in self.result["harmonics"]:
+            amp, ph = h["amplitude"], h["phase"]
+            se_a, se_p = h["se_amplitude"], h["se_phase"]
+            label = f"{h['variable']}, k={h['k']}"
+            rows.append(
+                (label, _fmt(amp), _fmt(se_a), _ci(amp, se_a), _fmt(ph), _fmt(se_p), _ci(ph, se_p))
+            )
+
+        headers = (
+            "term",
+            "amplitude",
+            "SE",
+            "CI[2.5%, 97.5%]",
+            "phase",
+            "SE",
+            "CI[2.5%, 97.5%]",
+        )
+        widths = [
+            max(len(h), max(len(r[i]) for r in rows)) for i, h in enumerate(headers)
+        ]
+
+        print("\nHarmonic decomposition:")
+        line = "  ".join(f"{h:<{w}s}" for h, w in zip(headers, widths))
+        print(line)
+        print("-" * len(line))
+        for r in rows:
+            print("  ".join(f"{c:<{w}s}" for c, w in zip(r, widths)))
         print(
-            f"p1: {self.result['p_values'][0]:.5f}, p2: {self.result['p_values'][1]:.5f}"
+            "Phase in radians; SEs and CIs from the delta method on (cos, sin) "
+            "coefficients.\n"
         )
 
-        print(f"\n{self.result['message']}\n")
+    def plot(
+        self,
+        figsize: Optional[Tuple[float, float]] = None,
+        n_curve: int = 200,
+        ci: bool = True,
+        pi: bool = False,
+        level: float = 0.95,
+        axes=None,
+    ):
+        """Two-panel diagnostic figure.
+
+        Left:  scatter (θ, y) with the fitted curve over the data's θ range,
+               optionally with confidence and/or prediction bands.
+        Right: residuals vs fitted values.
+
+        For models with extra non-circular covariates (e.g. ``y ~
+        harmonic(θ) + temperature``), the curve is drawn fixing those
+        covariates at their column means.
+
+        Parameters
+        ----------
+        figsize : tuple, optional
+            Matplotlib figure size; defaults to ``(11, 4.5)``.
+        n_curve : int
+            Number of θ points used to draw the fitted curve.
+        ci, pi : bool
+            Whether to shade a confidence band (``ci``) and/or prediction
+            band (``pi``) at the requested ``level``.
+        level : float
+            Coverage probability for the bands (default 0.95).
+        axes : sequence of matplotlib Axes, optional
+            Two pre-existing axes to draw into. If omitted, a fresh figure
+            is created.
+
+        Returns
+        -------
+        matplotlib.figure.Figure
+        """
+        import matplotlib.pyplot as plt
+
+        if not self.result["harmonics"]:
+            raise ValueError(
+                "plot() requires at least one matched cos/sin pair "
+                "(harmonic decomposition)."
+            )
+
+        theta_var = self.result["harmonics"][0]["variable"]
+        theta_data = self.data[theta_var].to_numpy()
+        y_data = self.data[self.response].to_numpy()
+        fitted = self.result["fitted"]
+        residuals = self.result["residuals"]
+
+        # Build a θ grid spanning the data; hold any other covariates at their mean.
+        t_lo = float(min(theta_data.min(), 0.0))
+        t_hi = float(max(theta_data.max(), 2 * np.pi))
+        theta_grid = np.linspace(t_lo, t_hi, n_curve)
+        grid: dict = {theta_var: theta_grid}
+        for col in self.data.columns:
+            if col in (theta_var, self.response):
+                continue
+            series = self.data[col]
+            if series.dtype.is_numeric():
+                grid[col] = np.full(n_curve, float(series.mean()))
+            else:
+                # Hold non-numeric columns at their mode.
+                grid[col] = [series.mode()[0]] * n_curve
+        grid_df = pl.DataFrame(grid)
+
+        yhat_df = self.lm_fit.predict(grid_df)
+        yhat = np.asarray(yhat_df.to_numpy()).ravel()
+        alpha = 1.0 - level
+        ci_lo = ci_hi = pi_lo = pi_hi = None
+        if ci:
+            arr = self.lm_fit.compute_ci_yhat(yhat=yhat_df, Xnew=grid_df, alpha=alpha).to_numpy()
+            ci_lo, ci_hi = arr[:, 0], arr[:, 1]
+        if pi:
+            arr = self.lm_fit.compute_pi_yhat(yhat=yhat_df, Xnew=grid_df, alpha=alpha).to_numpy()
+            pi_lo, pi_hi = arr[:, 0], arr[:, 1]
+
+        if axes is None:
+            fig, axes = plt.subplots(1, 2, figsize=figsize or (11, 4.5))
+        else:
+            axes = list(axes)
+            if len(axes) != 2:
+                raise ValueError("`axes` must be a sequence of length 2.")
+            fig = axes[0].figure
+
+        ax = axes[0]
+        if pi_lo is not None:
+            ax.fill_between(
+                theta_grid, pi_lo, pi_hi,
+                color="C1", alpha=0.15, label=f"{int(level*100)}% PI",
+            )
+        if ci_lo is not None:
+            ax.fill_between(
+                theta_grid, ci_lo, ci_hi,
+                color="C1", alpha=0.30, label=f"{int(level*100)}% CI",
+            )
+        ax.plot(theta_grid, yhat, color="C1", lw=2, label="fit")
+        ax.scatter(theta_data, y_data, color="C0", s=20, alpha=0.6, edgecolors="none", label="data")
+        ax.set_xlabel(theta_var)
+        ax.set_ylabel(self.response)
+        ax.set_title("Fit overlay")
+        ax.legend(loc="best", frameon=False)
+
+        # If the grid covers a full 2π span, mark the canonical ticks.
+        if t_lo <= 0 and t_hi >= 2 * np.pi:
+            ax.set_xticks([0, np.pi / 2, np.pi, 3 * np.pi / 2, 2 * np.pi])
+            ax.set_xticklabels(["0", "π/2", "π", "3π/2", "2π"])
+
+        ax = axes[1]
+        ax.scatter(fitted, residuals, color="C0", s=20, alpha=0.6, edgecolors="none")
+        ax.axhline(0.0, color="k", lw=0.5)
+        ax.set_xlabel("Fitted")
+        ax.set_ylabel("Residual")
+        ax.set_title("Residuals vs fitted")
+
+        fig.tight_layout()
+        return fig
diff --git a/pycircstat2/utils.py b/pycircstat2/utils.py
index 7c81eb6..7a89488 100644
--- a/pycircstat2/utils.py
+++ b/pycircstat2/utils.py
@@ -4,7 +4,7 @@
 
 import numpy as np
 import pandas as pd
-from scipy.special import i0, i1
+from scipy.special import i0e, i1e
 
 
 def data2rad(
@@ -231,9 +231,13 @@ def rotate_data(alpha: np.ndarray, angle: float, unit: str = "radian") -> np.nda
 
 
 def A1(kappa: np.ndarray) -> np.ndarray:
-    return i1(kappa) / i0(kappa)
+    # i1e(κ)/i0e(κ) = (i1(κ) e^-κ)/(i0(κ) e^-κ) — stable for large κ where i0/i1 overflow.
+    return i1e(kappa) / i0e(kappa)
 
 def A1inv(R: float) -> float:
+    # A1 maps kappa>=0 to [0, 1); clamp R to that range to avoid the
+    # singularity at R=1 in the high-concentration branch.
+    R = min(max(R, 0.0), 1.0 - 1e-12)
     if 0 <= R < 0.53:
         return 2 * R + R**3 + (5 * R**5) / 6
     elif R < 0.85:
diff --git a/pyproject.toml b/pyproject.toml
index 29a1c42..ed6841a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pycircstat2"
-version = "0.1.15"
+version = "0.1.16"
 description = "Circular statistcs with Python."
 authors = [
 ]
diff --git a/tests/test_regression.py b/tests/test_regression.py
index 013d06b..ae1267a 100644
--- a/tests/test_regression.py
+++ b/tests/test_regression.py
@@ -1,8 +1,21 @@
 import numpy as np
 import pandas as pd
+import pytest
 
 from pycircstat2 import load_data
-from pycircstat2.regression import CCRegression, CLRegression
+from pycircstat2.regression import CCRegression, CLRegression, LCRegression
+from pycircstat2.utils import A1inv
+
+
+def _lung_dataframe(drop_feb_outliers: bool = True) -> pd.DataFrame:
+    """Pewsey, Neuhäuser & Ruxton (2014) §8.4.1 lung-disease deaths."""
+    df = load_data("lung_deaths", source="pewsey").copy()
+    df["theta"] = (np.pi / 6) * df["month"].to_numpy()
+    df = df.rename(columns={"deaths": "y"})
+    if drop_feb_outliers:
+        df = df[~((df["month"] == 2) & df["year"].isin([1976, 1979]))]
+        df = df.reset_index(drop=True)
+    return df
 
 
 def test_cc_regression_against_r():
@@ -168,3 +181,491 @@ def test_cl_regression_against_r():
     assert np.isclose(
         result["log_likelihood"], expected_log_likelihood, atol=1e-2
     ), f"Expected log-likelihood: {expected_log_likelihood}, got: {result['log_likelihood']}"
+
+
+def _simulate_cl(seed: int = 0, n: int = 400):
+    rng = np.random.default_rng(seed)
+    X = rng.normal(size=(n, 1))
+    mu_true, beta_true, kappa_true = 0.7, np.array([0.9]), 5.0
+    eps = rng.vonmises(0, kappa_true, size=n)
+    theta = mu_true + 2 * np.arctan(X @ beta_true) + eps
+    return X, theta, mu_true, beta_true, kappa_true
+
+
+def test_mixed_matches_mean_when_kappa_is_constant():
+    """With constant true κ, mixed and mean should agree on β/μ to high precision."""
+    X, theta, _, _, _ = _simulate_cl()
+
+    m_mean = CLRegression(theta=theta, X=X, model_type="mean", tol=1e-10, max_iter=500)
+    m_mixed = CLRegression(theta=theta, X=X, model_type="mixed", tol=1e-10, max_iter=500)
+
+    np.testing.assert_allclose(
+        m_mixed.result["beta"], m_mean.result["beta"], atol=5e-3
+    )
+    np.testing.assert_allclose(m_mixed.result["mu"], m_mean.result["mu"], atol=5e-3)
+    # exp(α) should be near the mean model's scalar κ since γ should be ≈ 0
+    np.testing.assert_allclose(
+        np.exp(m_mixed.result["alpha"]), m_mean.result["kappa"], rtol=0.1
+    )
+    assert abs(m_mixed.result["gamma"][0]) < 0.1
+
+
+def test_mixed_recovers_true_parameters():
+    """Mixed model should recover the simulation parameters within sampling error."""
+    X, theta, mu_true, beta_true, kappa_true = _simulate_cl(seed=1, n=800)
+    m = CLRegression(theta=theta, X=X, model_type="mixed", tol=1e-10, max_iter=500)
+    np.testing.assert_allclose(m.result["beta"], beta_true, atol=0.15)
+    np.testing.assert_allclose(m.result["mu"], mu_true, atol=0.1)
+    np.testing.assert_allclose(np.exp(m.result["alpha"]), kappa_true, rtol=0.25)
+
+
+def test_kappa_model_fits_and_predicts_constant_mean():
+    """Kappa-only model: κ depends on X but conditional mean is constant μ."""
+    X, theta, _, _, _ = _simulate_cl(seed=2, n=300)
+    m = CLRegression(theta=theta, X=X, model_type="kappa", tol=1e-8, max_iter=200)
+
+    assert np.all(np.isfinite(m.result["kappa"]))
+    assert np.all(m.result["kappa"] > 0)
+
+    pred = m.predict(X)
+    assert pred.shape == (X.shape[0],)
+    np.testing.assert_allclose(pred, np.mod(m.result["mu"], 2 * np.pi))
+
+
+def test_predict_mean_model_round_trip():
+    X, theta, _, _, _ = _simulate_cl(seed=3, n=200)
+    m = CLRegression(theta=theta, X=X, model_type="mean", tol=1e-10)
+    pred = m.predict(X)
+    assert pred.shape == theta.shape
+    assert np.all(np.isfinite(pred))
+
+
+def test_se_kappa_delta_method_shape_and_finiteness():
+    X, theta, _, _, _ = _simulate_cl(seed=4, n=300)
+    for model_type in ("kappa", "mixed"):
+        m = CLRegression(theta=theta, X=X, model_type=model_type, tol=1e-8, max_iter=300)
+        se = m.result["se_kappa"]
+        assert se.shape == (X.shape[0],)
+        assert np.all(np.isfinite(se))
+        assert np.all(se > 0)
+
+
+def test_a1inv_clamps_at_unit_radius():
+    # A1 maps κ≥0 to [0,1); A1inv at R≥1 must not explode.
+    assert np.isfinite(A1inv(1.0))
+    assert np.isfinite(A1inv(1.5))
+    assert A1inv(0.0) == 0.0
+
+
+def test_cc_regression_rejects_oversize_order():
+    rng = np.random.default_rng(0)
+    theta = rng.uniform(0, 2 * np.pi, 5)
+    x = rng.uniform(0, 2 * np.pi, 5)
+    with pytest.raises(ValueError, match="more than"):
+        CCRegression(theta=theta, x=x, order=5)
+
+
+def test_cc_regression_exposes_residual_kappa():
+    df = load_data("milwaukee", source="jammalamadaka")
+    ctheta = np.deg2rad(df["theta"].values)
+    cpsi = np.deg2rad(df["psi"].values)
+    m = CCRegression(theta=ctheta, x=cpsi, order=2)
+    assert "kappa" in m.result and "A_k" in m.result
+    assert np.isfinite(m.result["kappa"]) and m.result["kappa"] >= 0
+    assert -1 <= m.result["A_k"] <= 1
+
+
+def test_a1_stable_at_extreme_kappa():
+    from pycircstat2.utils import A1
+
+    # i0/i1 overflow around κ ≈ 710; A1 must remain finite via i0e/i1e.
+    for k in (700.0, 5_000.0, 1e6):
+        val = float(A1(k))
+        assert np.isfinite(val)
+        assert 0.0 < val < 1.0
+
+
+def test_log_likelihood_stable_at_high_concentration():
+    rng = np.random.default_rng(0)
+    n = 200
+    X = rng.normal(size=(n, 1))
+    theta = 0.5 + rng.vonmises(0, 50.0, n)
+    m = CLRegression(theta=theta, X=X, model_type="kappa", tol=1e-8, max_iter=300)
+    assert np.isfinite(m.result["log_likelihood"])
+    assert np.all(np.isfinite(m.result["kappa"]))
+
+
+def test_formula_parser_rejects_malformed_formulas():
+    df = pd.DataFrame({"y": [0.1, 0.2], "x": [1.0, 2.0]})
+    with pytest.raises(ValueError, match="exactly one '~'"):
+        CLRegression(formula="y ~ x ~ z", data=df)
+    with pytest.raises(ValueError, match="No predictors"):
+        CLRegression(formula="y ~ ", data=df)
+
+
+def test_cl_plot_mean_model_1d():
+    import matplotlib
+
+    matplotlib.use("Agg")
+    df = load_data("B20", source="fisher")
+    data = pd.DataFrame({"X": df["x"].values, "θ": np.deg2rad(df["θ"].values)})
+    m = CLRegression(formula="θ ~ X", data=data, model_type="mean")
+    fig = m.plot()
+    titles = [ax.get_title() for ax in fig.axes]
+    assert "Fit overlay" in titles
+    assert "Residuals vs X" in titles
+    overlay = next(ax for ax in fig.axes if ax.get_title() == "Fit overlay")
+    ylo, yhi = overlay.get_ylim()
+    assert ylo == 0.0 and np.isclose(yhi, 4 * np.pi)
+
+
+def test_cl_plot_kappa_only_shows_kappa_curve():
+    import matplotlib
+
+    matplotlib.use("Agg")
+    df = load_data("B20", source="fisher")
+    data = pd.DataFrame({"X": df["x"].values, "θ": np.deg2rad(df["θ"].values)})
+    m = CLRegression(formula="θ ~ X", data=data, model_type="kappa")
+    fig = m.plot()
+    titles = [ax.get_title() for ax in fig.axes]
+    assert "Fitted concentration" in titles
+    kappa_ax = next(ax for ax in fig.axes if ax.get_title() == "Fitted concentration")
+    # κ curve must be strictly positive.
+    line = kappa_ax.get_lines()[0]
+    ys = line.get_ydata()
+    assert np.all(ys > 0)
+
+
+def test_cl_predict_kappa_kappa_and_mixed():
+    rng = np.random.default_rng(0)
+    n = 200
+    X = rng.normal(size=(n, 1))
+    theta = 0.5 + rng.vonmises(0, 50.0, n)
+    for model_type in ("kappa", "mixed"):
+        m = CLRegression(theta=theta, X=X, model_type=model_type, tol=1e-8, max_iter=200)
+        kappa_pred = m.predict_kappa(np.array([0.0, 1.0, -1.0]))
+        assert kappa_pred.shape == (3,)
+        assert np.all(kappa_pred > 0) and np.all(np.isfinite(kappa_pred))
+        # κ at X=0 must equal exp(α̂).
+        np.testing.assert_allclose(
+            kappa_pred[0], np.exp(m.result["alpha"]), atol=1e-10
+        )
+
+
+def test_cl_predict_kappa_rejects_mean_model():
+    rng = np.random.default_rng(0)
+    n = 100
+    X = rng.normal(size=(n, 1))
+    theta = 0.5 + 2 * np.arctan(X[:, 0] * 0.3) + rng.vonmises(0, 5.0, n)
+    m = CLRegression(theta=theta, X=X, model_type="mean")
+    with pytest.raises(ValueError, match="model_type in"):
+        m.predict_kappa(np.array([0.0]))
+
+
+def test_lc_harmonic_positional_k():
+    """Both `harmonic(theta, k=2)` and `harmonic(theta, 2)` must work."""
+    df = _lung_dataframe(drop_feb_outliers=True)
+    m_kw = LCRegression("y ~ harmonic(theta, k=2)", df)
+    m_pos = LCRegression("y ~ harmonic(theta, 2)", df)
+    assert m_kw.expanded_formula == m_pos.expanded_formula
+    np.testing.assert_allclose(
+        list(m_kw.result["coefficients"].values()),
+        list(m_pos.result["coefficients"].values()),
+        atol=1e-12,
+    )
+
+
+def test_cl_summary_does_not_print_mean_se(capsys):
+    """Per-obs SEs are correlated; averaging them is meaningless. Drop it."""
+    rng = np.random.default_rng(0)
+    n = 50
+    X = rng.normal(size=(n, 1))
+    theta = 0.5 + rng.vonmises(0, 5.0, n)
+    m = CLRegression(theta=theta, X=X, model_type="kappa")
+    m.summary()
+    out = capsys.readouterr().out
+    # Old format included "Mean: ... (SE: ...)"; new format drops the SE.
+    mean_lines = [ln for ln in out.splitlines() if ln.strip().startswith("Mean:")]
+    assert mean_lines, "summary should print a Mean kappa line"
+    for ln in mean_lines:
+        assert "SE" not in ln
+
+
+def test_cl_plot_multi_feature_fallback():
+    import matplotlib
+
+    matplotlib.use("Agg")
+    rng = np.random.default_rng(0)
+    n = 100
+    X = rng.normal(size=(n, 2))
+    theta = 0.7 + 2 * np.arctan(X @ np.array([0.5, -0.3])) + rng.vonmises(0, 5.0, n)
+    m = CLRegression(theta=theta, X=X, model_type="mean")
+    fig = m.plot()
+    titles = [ax.get_title() for ax in fig.axes]
+    assert "Residuals vs fitted" in titles
+    assert "Residual histogram" in titles
+
+
+def test_cc_predict_round_trip_on_training_data():
+    df = load_data("milwaukee", source="jammalamadaka")
+    ctheta = np.deg2rad(df["theta"].values)
+    cpsi = np.deg2rad(df["psi"].values)
+    m = CCRegression(theta=ctheta, x=cpsi, order=2)
+    pred = m.predict(cpsi)
+    diff = np.angle(np.exp(1j * (pred - m.result["fitted"])))
+    assert np.max(np.abs(diff)) < 1e-10
+
+
+def test_cc_predict_rejects_wrong_feature_count():
+    rng = np.random.default_rng(0)
+    n = 30
+    theta = rng.uniform(0, 2 * np.pi, n)
+    x = rng.uniform(0, 2 * np.pi, (n, 2))
+    m = CCRegression(theta=theta, x=x, order=1)
+    with pytest.raises(ValueError, match="Expected 2"):
+        m.predict(np.ones(5))
+
+
+def test_cc_plot_single_feature_two_panels():
+    import matplotlib
+
+    matplotlib.use("Agg")
+    df = load_data("milwaukee", source="jammalamadaka")
+    ctheta = np.deg2rad(df["theta"].values)
+    cpsi = np.deg2rad(df["psi"].values)
+    m = CCRegression(theta=ctheta, x=cpsi, order=2)
+    fig = m.plot()
+    titles = [ax.get_title() for ax in fig.axes]
+    assert "Fit overlay" in titles
+    assert "Residuals vs predictor" in titles
+    overlay_ax = next(ax for ax in fig.axes if ax.get_title() == "Fit overlay")
+    # y-axis should span [0, 4π] for the stacked-copy display.
+    ylo, yhi = overlay_ax.get_ylim()
+    assert ylo == 0.0 and np.isclose(yhi, 4 * np.pi)
+
+
+def test_cc_plot_multi_feature_fallback():
+    import matplotlib
+
+    matplotlib.use("Agg")
+    rng = np.random.default_rng(0)
+    n = 80
+    x = rng.uniform(0, 2 * np.pi, (n, 2))
+    theta = np.mod(0.5 + 0.3 * np.sin(x[:, 0]) + 0.2 * np.cos(x[:, 1]), 2 * np.pi)
+    m = CCRegression(theta=theta, x=x, order=1)
+    fig = m.plot()
+    titles = [ax.get_title() for ax in fig.axes]
+    assert "Residuals vs fitted" in titles
+    assert "Residual histogram" in titles
+
+
+def test_cc_summary_label_widths(capsys):
+    rng = np.random.default_rng(0)
+    n = 60
+    theta = rng.uniform(0, 2 * np.pi, n)
+    x = rng.uniform(0, 2 * np.pi, n)
+    m = CCRegression(theta=theta, x=x, order=4)
+    m.summary()
+    captured = capsys.readouterr().out
+    # Long labels like "cos(x1,k=4)" must appear intact (not truncated).
+    assert "cos(x1,k=4)" in captured
+    assert "sin(x1,k=4)" in captured
+
+
+# ----------------------------- LCRegression -------------------------------
+
+
+def test_lc_regression_against_pewsey_lung_disease():
+    """§8.4.1 reduced extended model: y ~ cos(θ) + sin(θ) + sin(2θ)."""
+    df = _lung_dataframe(drop_feb_outliers=True)
+    m = LCRegression("y ~ cos(theta) + sin(theta) + sin(2*theta)", df)
+    coefs = m.result["coefficients"]
+    assert np.isclose(coefs["(Intercept)"], 2125.12, atol=1e-1)
+    assert np.isclose(coefs["cos(theta)"], 454.18, atol=1e-1)
+    assert np.isclose(coefs["sin(theta)"], 601.96, atol=1e-1)
+    assert np.isclose(coefs["sin(2 * theta)"], 108.69, atol=1e-1)
+    assert np.isclose(m.result["sigma"], 171.3, atol=1e-1)
+    assert np.isclose(m.result["r_squared"], 0.9093, atol=1e-3)
+
+
+def test_lc_marker_matches_explicit():
+    """harmonic(theta, k=K) must produce identical fit to the explicit form."""
+    rng = np.random.default_rng(0)
+    n = 240
+    theta = np.linspace(0, 2 * np.pi, n, endpoint=False)
+    y = (
+        2.0
+        + 1.5 * np.cos(theta - 0.7)
+        + 0.6 * np.cos(2 * theta - 1.2)
+        + rng.normal(0, 0.2, n)
+    )
+    df = pd.DataFrame({"y": y, "theta": theta})
+
+    marker = LCRegression("y ~ harmonic(theta, k=2)", df)
+    explicit = LCRegression(
+        "y ~ cos(theta) + sin(theta) + cos(2*theta) + sin(2*theta)", df
+    )
+    np.testing.assert_allclose(
+        list(marker.result["coefficients"].values()),
+        list(explicit.result["coefficients"].values()),
+        atol=1e-10,
+    )
+    assert marker.expanded_formula == explicit.expanded_formula
+    np.testing.assert_allclose(
+        [h["amplitude"] for h in marker.result["harmonics"]],
+        [h["amplitude"] for h in explicit.result["harmonics"]],
+        atol=1e-10,
+    )
+
+
+def test_lc_amplitude_phase_recovery():
+    """Generated data with known γ₁ and φ₁ — recovered to good precision."""
+    rng = np.random.default_rng(1)
+    n = 1000
+    theta = rng.uniform(0, 2 * np.pi, n)
+    true_amp, true_phase = 2.5, 0.9
+    y = 5.0 + true_amp * np.cos(theta - true_phase) + rng.normal(0, 0.1, n)
+    df = pd.DataFrame({"y": y, "theta": theta})
+
+    m = LCRegression("y ~ harmonic(theta)", df)
+    h = m.result["harmonics"]
+    assert len(h) == 1
+    assert h[0]["k"] == 1
+    assert np.isclose(h[0]["amplitude"], true_amp, atol=0.05)
+    assert np.isclose(h[0]["phase"], true_phase, atol=0.05)
+
+
+def test_lc_marker_with_extra_covariate():
+    """harmonic(theta) + temperature: marker expands, covariate passes through."""
+    rng = np.random.default_rng(2)
+    n = 300
+    theta = rng.uniform(0, 2 * np.pi, n)
+    temperature = rng.normal(20, 5, n)
+    y = 1.0 + 2.0 * np.cos(theta - 0.4) + 0.3 * temperature + rng.normal(0, 0.2, n)
+    df = pd.DataFrame({"y": y, "theta": theta, "temperature": temperature})
+
+    m = LCRegression("y ~ harmonic(theta) + temperature", df)
+    coefs = m.result["coefficients"]
+    assert "temperature" in coefs
+    assert np.isclose(coefs["temperature"], 0.3, atol=0.05)
+    h = m.result["harmonics"][0]
+    assert np.isclose(h["amplitude"], 2.0, atol=0.05)
+
+
+def test_lc_predict_round_trip():
+    df = _lung_dataframe(drop_feb_outliers=True)
+    m = LCRegression("y ~ harmonic(theta, k=2)", df)
+    pred = m.predict(df)
+    assert pred.shape == (len(df),)
+    np.testing.assert_allclose(pred, m.result["fitted"], atol=1e-10)
+
+
+def test_lc_skew_and_flat_not_implemented():
+    df = pd.DataFrame({"y": [1.0, 2.0, 3.0], "theta": [0.0, 1.0, 2.0]})
+    with pytest.raises(NotImplementedError, match="hea.nls"):
+        LCRegression("y ~ skew(theta)", df)
+    with pytest.raises(NotImplementedError, match="hea.nls"):
+        LCRegression("y ~ flat(theta)", df)
+
+
+def test_lc_formula_validation():
+    df = pd.DataFrame({"y": [1.0, 2.0, 3.0], "theta": [0.0, 1.0, 2.0]})
+    with pytest.raises(ValueError, match="'~'"):
+        LCRegression("not a formula", df)
+    with pytest.raises(ValueError, match="positive integer"):
+        LCRegression("y ~ harmonic(theta, k=0)", df)
+
+
+def test_lc_accepts_polars_data():
+    import polars as pl
+
+    df = _lung_dataframe(drop_feb_outliers=True)
+    m_pd = LCRegression("y ~ harmonic(theta)", df)
+    m_pl = LCRegression("y ~ harmonic(theta)", pl.from_pandas(df))
+    np.testing.assert_allclose(
+        list(m_pd.result["coefficients"].values()),
+        list(m_pl.result["coefficients"].values()),
+        atol=1e-12,
+    )
+
+
+def test_lc_harmonic_se_and_ci_present():
+    """Delta-method SEs for amplitude/phase should be finite and positive."""
+    df = _lung_dataframe(drop_feb_outliers=True)
+    m = LCRegression("y ~ harmonic(theta, k=2)", df)
+    for h in m.result["harmonics"]:
+        assert h["se_amplitude"] is not None and h["se_amplitude"] > 0
+        assert h["se_phase"] is not None and h["se_phase"] > 0
+        # Sanity: SE_amp should not exceed the amplitude itself by orders of magnitude.
+        assert h["se_amplitude"] < 10 * h["amplitude"]
+
+
+def test_lc_accepts_unicode_identifiers():
+    """Greek/Unicode column names should work in formulas (e.g. `θ`)."""
+    df = _lung_dataframe(drop_feb_outliers=True)
+    df_unicode = df.rename(columns={"theta": "θ"})
+    m = LCRegression("y ~ harmonic(θ, k=2)", df_unicode)
+    assert m.expanded_formula == "y ~ cos(θ) + sin(θ) + cos(2*θ) + sin(2*θ)"
+    assert np.isclose(m.result["r_squared"], 0.9094, atol=1e-3)
+    assert all(h["variable"] == "θ" for h in m.result["harmonics"])
+
+
+def test_lc_plot_returns_figure_with_two_panels():
+    import matplotlib
+
+    matplotlib.use("Agg")
+    df = _lung_dataframe(drop_feb_outliers=True)
+    m = LCRegression("y ~ harmonic(theta, k=2)", df)
+    fig = m.plot(ci=True, pi=True)
+    titles = [ax.get_title() for ax in fig.axes]
+    assert "Fit overlay" in titles
+    assert "Residuals vs fitted" in titles
+    # Fit overlay axes should at least contain fit + data + CI + PI artists.
+    overlay_ax = next(ax for ax in fig.axes if ax.get_title() == "Fit overlay")
+    labels = [ln.get_label() for ln in overlay_ax.get_lines()]
+    assert "fit" in labels
+
+
+def test_lc_plot_with_extra_covariate_holds_at_mean():
+    """When extra covariates exist, the fit curve fixes them at the column mean."""
+    import matplotlib
+
+    matplotlib.use("Agg")
+    rng = np.random.default_rng(0)
+    n = 200
+    theta = rng.uniform(0, 2 * np.pi, n)
+    temp = rng.normal(20, 5, n)
+    y = 2 + 1.5 * np.cos(theta - 0.5) + 0.1 * temp + rng.normal(0, 0.2, n)
+    df = pd.DataFrame({"y": y, "theta": theta, "temp": temp})
+    m = LCRegression("y ~ harmonic(theta) + temp", df)
+    fig = m.plot(ci=False, pi=False)
+    overlay_ax = next(ax for ax in fig.axes if ax.get_title() == "Fit overlay")
+    fit_line = next(ln for ln in overlay_ax.get_lines() if ln.get_label() == "fit")
+    xs, ys = fit_line.get_xdata(), fit_line.get_ydata()
+    # At the curve midpoint of θ, the value should match the analytical
+    # "fit at theta=π, temp=mean" prediction within numerical tolerance.
+    coefs = m.result["coefficients"]
+    expected_at_pi = (
+        coefs["(Intercept)"]
+        + coefs["cos(theta)"] * np.cos(np.pi)
+        + coefs["sin(theta)"] * np.sin(np.pi)
+        + coefs["temp"] * float(temp.mean())
+    )
+    idx = np.argmin(np.abs(xs - np.pi))
+    # Tolerance reflects the grid spacing (200 points across [0, 2π]).
+    assert np.isclose(ys[idx], expected_at_pi, atol=0.05)
+
+
+def test_lc_summary_includes_lm_block_and_harmonic_table(capsys):
+    df = _lung_dataframe(drop_feb_outliers=True)
+    m = LCRegression("y ~ harmonic(theta, k=2)", df)
+    m.summary()
+    out = capsys.readouterr().out
+    # hea.lm summary content
+    assert "Coefficients:" in out
+    assert "Pr(>|t|)" in out
+    assert "R-Squared" in out
+    # Our additions
+    assert "Harmonic decomposition" in out
+    assert "amplitude" in out
+    assert "phase" in out
diff --git a/uv.lock b/uv.lock
index e211c56..9d9222c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1814,7 +1814,7 @@ wheels = [
 
 [[package]]
 name = "pycircstat2"
-version = "0.1.15"
+version = "0.1.16"
 source = { editable = "." }
 dependencies = [
     { name = "hea" },