From 371d69346db6debacc756003b6f4aede1f030723 Mon Sep 17 00:00:00 2001
From: GStechschulte <stechschulteg@gmail.com>
Date: Fri, 13 Oct 2023 16:03:22 +0200
Subject: [PATCH] update docs to reflect API change

---
 docs/notebooks/mister_p.ipynb                 |  354 ++-
 docs/notebooks/plot_predictions.ipynb         | 2300 ++++++++++-------
 docs/notebooks/zero_inflated_regression.ipynb |   16 +-
 3 files changed, 1605 insertions(+), 1065 deletions(-)

diff --git a/docs/notebooks/mister_p.ipynb b/docs/notebooks/mister_p.ipynb
index 35b866896..3f8ca6579 100644
--- a/docs/notebooks/mister_p.ipynb
+++ b/docs/notebooks/mister_p.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -266,7 +266,7 @@
        "19    Dionysus            1          1        0                        1"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -317,7 +317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -368,7 +368,7 @@
        "1          0.538462"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -388,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -414,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -469,7 +469,7 @@
        "1                   12         0.6"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -490,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -545,7 +545,7 @@
        "1            0.25  0.666667"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -571,7 +571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -634,7 +634,7 @@
        "1            0.25  0.666667  0.4*0.25 + 0.6*0.66               0.5"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -657,7 +657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -696,7 +696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -705,11 +705,54 @@
      "text": [
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (4 chains in 4 jobs)\n",
       "NUTS: [Outcome_sigma, Intercept, Treatment]\n"
      ]
     },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:00&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stderr",
      "output_type": "stream",
@@ -721,6 +764,50 @@
       "NUTS: [Outcome_sigma, Intercept, Treatment, Risk_Strata, Treatment_x_Risk_Strata]\n"
      ]
     },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:01&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stderr",
      "output_type": "stream",
@@ -747,7 +834,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -785,38 +872,38 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Intercept</th>\n",
-       "      <td>0.428</td>\n",
-       "      <td>0.203</td>\n",
-       "      <td>0.060</td>\n",
-       "      <td>0.823</td>\n",
+       "      <td>0.433</td>\n",
+       "      <td>0.208</td>\n",
+       "      <td>0.052</td>\n",
+       "      <td>0.829</td>\n",
        "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>4840.0</td>\n",
-       "      <td>2982.0</td>\n",
+       "      <td>4351.0</td>\n",
+       "      <td>2913.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Treatment</th>\n",
-       "      <td>0.108</td>\n",
-       "      <td>0.252</td>\n",
-       "      <td>-0.357</td>\n",
+       "      <td>0.107</td>\n",
+       "      <td>0.261</td>\n",
+       "      <td>-0.399</td>\n",
        "      <td>0.584</td>\n",
        "      <td>0.004</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>4258.0</td>\n",
-       "      <td>2731.0</td>\n",
+       "      <td>4766.0</td>\n",
+       "      <td>2830.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Outcome_sigma</th>\n",
-       "      <td>0.542</td>\n",
-       "      <td>0.092</td>\n",
+       "      <td>0.545</td>\n",
+       "      <td>0.094</td>\n",
        "      <td>0.388</td>\n",
-       "      <td>0.713</td>\n",
+       "      <td>0.726</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>4073.0</td>\n",
-       "      <td>2488.0</td>\n",
+       "      <td>4253.0</td>\n",
+       "      <td>3314.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -825,17 +912,17 @@
       ],
       "text/plain": [
        "                mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
-       "Intercept      0.428  0.203   0.060    0.823      0.003    0.002    4840.0   \n",
-       "Treatment      0.108  0.252  -0.357    0.584      0.004    0.004    4258.0   \n",
-       "Outcome_sigma  0.542  0.092   0.388    0.713      0.001    0.001    4073.0   \n",
+       "Intercept      0.433  0.208   0.052    0.829      0.003    0.002    4351.0   \n",
+       "Treatment      0.107  0.261  -0.399    0.584      0.004    0.004    4766.0   \n",
+       "Outcome_sigma  0.545  0.094   0.388    0.726      0.001    0.001    4253.0   \n",
        "\n",
        "               ess_tail  r_hat  \n",
-       "Intercept        2982.0    1.0  \n",
-       "Treatment        2731.0    1.0  \n",
-       "Outcome_sigma    2488.0    1.0  "
+       "Intercept        2913.0    1.0  \n",
+       "Treatment        2830.0    1.0  \n",
+       "Outcome_sigma    3314.0    1.0  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -846,7 +933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -884,62 +971,62 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Intercept</th>\n",
-       "      <td>0.254</td>\n",
-       "      <td>0.261</td>\n",
-       "      <td>-0.233</td>\n",
-       "      <td>0.743</td>\n",
-       "      <td>0.005</td>\n",
+       "      <td>0.246</td>\n",
+       "      <td>0.271</td>\n",
+       "      <td>-0.250</td>\n",
+       "      <td>0.761</td>\n",
+       "      <td>0.006</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>2710.0</td>\n",
-       "      <td>2648.0</td>\n",
+       "      <td>2234.0</td>\n",
+       "      <td>2512.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Treatment</th>\n",
        "      <td>-0.001</td>\n",
-       "      <td>0.367</td>\n",
-       "      <td>-0.653</td>\n",
-       "      <td>0.730</td>\n",
-       "      <td>0.008</td>\n",
+       "      <td>0.378</td>\n",
+       "      <td>-0.701</td>\n",
+       "      <td>0.716</td>\n",
+       "      <td>0.009</td>\n",
        "      <td>0.006</td>\n",
-       "      <td>2312.0</td>\n",
-       "      <td>2648.0</td>\n",
+       "      <td>1922.0</td>\n",
+       "      <td>2064.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Risk_Strata</th>\n",
-       "      <td>0.405</td>\n",
-       "      <td>0.395</td>\n",
-       "      <td>-0.349</td>\n",
-       "      <td>1.119</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>2274.0</td>\n",
-       "      <td>2503.0</td>\n",
+       "      <td>0.416</td>\n",
+       "      <td>0.403</td>\n",
+       "      <td>-0.326</td>\n",
+       "      <td>1.177</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>2122.0</td>\n",
+       "      <td>2462.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Treatment_x_Risk_Strata</th>\n",
-       "      <td>0.010</td>\n",
-       "      <td>0.496</td>\n",
-       "      <td>-0.947</td>\n",
-       "      <td>0.939</td>\n",
-       "      <td>0.011</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>0.504</td>\n",
+       "      <td>-0.931</td>\n",
+       "      <td>0.959</td>\n",
+       "      <td>0.012</td>\n",
        "      <td>0.009</td>\n",
-       "      <td>1986.0</td>\n",
-       "      <td>2113.0</td>\n",
+       "      <td>1792.0</td>\n",
+       "      <td>2128.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Outcome_sigma</th>\n",
-       "      <td>0.531</td>\n",
-       "      <td>0.098</td>\n",
-       "      <td>0.367</td>\n",
-       "      <td>0.714</td>\n",
+       "      <td>0.533</td>\n",
+       "      <td>0.100</td>\n",
+       "      <td>0.368</td>\n",
+       "      <td>0.727</td>\n",
        "      <td>0.002</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2389.0</td>\n",
-       "      <td>2533.0</td>\n",
+       "      <td>2274.0</td>\n",
+       "      <td>1786.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -948,21 +1035,21 @@
       ],
       "text/plain": [
        "                          mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
-       "Intercept                0.254  0.261  -0.233    0.743      0.005    0.004   \n",
-       "Treatment               -0.001  0.367  -0.653    0.730      0.008    0.006   \n",
-       "Risk_Strata              0.405  0.395  -0.349    1.119      0.008    0.006   \n",
-       "Treatment_x_Risk_Strata  0.010  0.496  -0.947    0.939      0.011    0.009   \n",
-       "Outcome_sigma            0.531  0.098   0.367    0.714      0.002    0.001   \n",
+       "Intercept                0.246  0.271  -0.250    0.761      0.006    0.004   \n",
+       "Treatment               -0.001  0.378  -0.701    0.716      0.009    0.006   \n",
+       "Risk_Strata              0.416  0.403  -0.326    1.177      0.009    0.007   \n",
+       "Treatment_x_Risk_Strata  0.002  0.504  -0.931    0.959      0.012    0.009   \n",
+       "Outcome_sigma            0.533  0.100   0.368    0.727      0.002    0.001   \n",
        "\n",
        "                         ess_bulk  ess_tail  r_hat  \n",
-       "Intercept                  2710.0    2648.0    1.0  \n",
-       "Treatment                  2312.0    2648.0    1.0  \n",
-       "Risk_Strata                2274.0    2503.0    1.0  \n",
-       "Treatment_x_Risk_Strata    1986.0    2113.0    1.0  \n",
-       "Outcome_sigma              2389.0    2533.0    1.0  "
+       "Intercept                  2234.0    2512.0    1.0  \n",
+       "Treatment                  1922.0    2064.0    1.0  \n",
+       "Risk_Strata                2122.0    2462.0    1.0  \n",
+       "Treatment_x_Risk_Strata    1792.0    2128.0    1.0  \n",
+       "Outcome_sigma              2274.0    1786.0    1.0  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -973,12 +1060,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1011,7 +1098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1024,10 +1111,10 @@
     {
      "data": {
       "text/plain": [
-       "0.5068569705412103"
+       "0.496535899999178"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1041,7 +1128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1054,10 +1141,10 @@
     {
      "data": {
       "text/plain": [
-       "0.49944292437387866"
+       "0.4905238060849129"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1080,12 +1167,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 2000x600 with 2 Axes>"
       ]
@@ -1097,8 +1184,8 @@
    "source": [
     "fig, axs = plt.subplots(1, 2, figsize=(20, 6))\n",
     "axs = axs.flatten()\n",
-    "bmb.interpret.plot_predictions(reg, results, covariates=[\"Treatment\"], ax=axs[0])\n",
-    "bmb.interpret.plot_predictions(reg_strata, results_strata, covariates=[\"Treatment\"], ax=axs[1])\n",
+    "bmb.interpret.plot_predictions(reg, results, conditional=[\"Treatment\"], ax=axs[0])\n",
+    "bmb.interpret.plot_predictions(reg_strata, results_strata, conditional=[\"Treatment\"], ax=axs[1])\n",
     "axs[0].set_title(\"Non Stratified Regression \\n Model Predictions\")\n",
     "axs[1].set_title(\"Stratified Regression \\n Model Predictions\");"
    ]
@@ -1129,7 +1216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1334,7 +1421,7 @@
        "[5 rows x 526 columns]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1356,7 +1443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1453,7 +1540,7 @@
        "4       1.0    MA  White  -0.5  40-49            HS  202120533"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1591,7 +1678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1713,7 +1800,7 @@
        "48197  0.416893  Northeast   7.134465  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1748,12 +1835,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 2000x1000 with 5 Axes>"
       ]
@@ -1815,7 +1902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1867,7 +1954,7 @@
        "1  0.465000  0.007054  Biased Data"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1917,7 +2004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -2026,7 +2113,7 @@
        "4    ID  White   0.5  70+       Post-grad  17        11  0.683102   West"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2065,28 +2152,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (4 chains in 4 jobs)\n",
-      "NUTS: [Intercept, C(state), C(eth), C(edu), male, repvote]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 816 seconds.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "formula = \"\"\" p(abortion, n) ~ C(state) + C(eth) + C(edu) + male + repvote\"\"\"\n",
     "\n",
@@ -2110,7 +2178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2149,7 +2217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2187,7 +2255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2403,7 +2471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2422,7 +2490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4802,7 +4870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5035,7 +5103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5414,7 +5482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5446,7 +5514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5601,7 +5669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -8457,7 +8525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -8646,7 +8714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -8764,7 +8832,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.11.0"
   },
   "orig_nbformat": 4
  },
diff --git a/docs/notebooks/plot_predictions.ipynb b/docs/notebooks/plot_predictions.ipynb
index 2f2f66831..b4542db45 100644
--- a/docs/notebooks/plot_predictions.ipynb
+++ b/docs/notebooks/plot_predictions.ipynb
@@ -1,986 +1,1458 @@
 {
-    "cells": [
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "# Plot Conditional Adjusted Predictions\n",
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plot Conditional Adjusted Predictions\n",
+    "\n",
+    "This notebook shows how to use, and the capabilities, of the `plot_predictions` function. The `plot_predictions` function is a part of Bambi's sub-package `interpret` that features a set of tools used to interpret complex regression models that is inspired by the R package [marginaleffects](https://vincentarelbundock.github.io/marginaleffects/articles/predictions.html#conditional-adjusted-predictions-plot). "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpreting Generalized Linear Models\n",
+    "\n",
+    "The purpose of the _generalized linear model_ (GLM) is to unify the approaches needed to analyze data for which either: (1) the assumption of a linear relation between $x$ and $y$, or (2) the assumption of normal variation is not appropriate. GLMs are typically specified in three stages:\n",
+    "1. the linear predictor $\\eta = X\\beta$ where $X$ is an $n$ x $p$ matrix of explanatory variables.\n",
+    "2. the link function $g(\\cdot)$ that relates the linear predictor to the mean of the outcome variable $\\mu = g^{-1}(\\eta) = g^{-1}(X\\beta)$\n",
+    "3. the random component specifying the distribution of the outcome variable $y$ with mean $\\mathbb{E}(y|X) = \\mu$.\n",
+    "\n",
+    "Based on these three specifications, the mean of the distribution of $y$, given $X$, is determined by $X\\beta: \\mathbb{E}(y|X) = g^{-1}(X\\beta)$. \n",
+    "\n",
+    "GLMs are a broad family of models where the output $y$ is typically assumed to follow an exponential family distribution, e.g., Binomial, Poisson, Gamma, Exponential, and Normal. The job of the link function is to map the linear space of the model $X\\beta$ onto the non-linear space of a parameter like $\\mu$. Commonly used link function are the _logit_ and _log_ link. Also known as the _canonical_ link functions. This brief introduction to GLMs is not meant to be exhuastive, and another good starting point is the Bambi [Basic Building Blocks](https://bambinos.github.io/bambi/notebooks/how_bambi_works.html#Link-functions) example.\n",
+    "\n",
+    "Due to the link function, there are typically three quantities of interest to interpret in a GLM:\n",
+    "1. the linear predictor $\\eta$\n",
+    "2. the mean $\\mu = g^{-1}(\\eta)$\n",
+    "3. the response variable $Y \\sim \\mathcal{D}(\\mu, \\theta)$ where $\\mu$ is the mean parameter and $\\theta$ is (possibly) a vector that contains all the other \"nuissance\" parameters of the distribution.\n",
+    "\n",
+    "As modelers, we are usually more interested in interpreting (2) and (3). However, $\\mu$ is not always on the same scale of the response variable and can be more difficult to interpret. Rather, the response scale is a more interpretable scale. Additionally, it is often the case that modelers would like to analyze how a model parameter varies across a range of explanatory variable values. To achieve such an analysis, Bambi has taken inspiration from the R package marginaleffects, and implemented a `plot_predictions` function that plots the conditional adjusted predictions to aid in the interpretation of GLMs. Below, it is briefly discussed what are conditionally adjusted predictions, how they are computed, and ultimately how to use the `plot_predictions` function."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conditionally Adjusted Predictions\n",
+    "\n",
+    "Adjusted predictions refers to the outcome predicted by a fitted model on a specified scale for a given combination of values of the predictor variables, such as their observed values, their means, or some user specified grid of values. The specification of the scale to make the predictions, the link or response scale, refers to the scale used to estimate the model. In normal linear regression, the link scale and the response scale are identical, and therefore, the adjusted prediction is expressed as the mean value of the response variable at the given values of the predictor variables. On the other hand, a logistic regression's link and response scale are not identical. An adjusted prediction on the link scale will be represented as the log-odds of a successful response given values of the predictor variables. Whereas an adjusted prediction on the response scale gives the probability that the response variable equals 1. The conditional part of conditionally adjusted predictions represents the specific predictor(s) and its values we would like to condition on when plotting predictions."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Computing Adjusted Predictions\n",
+    "\n",
+    "The objective of plotting conditional adjusted predictions is to visualize how a parameter of the (conditional) response distribution varies as a function of (some) explanatory variables. In `predictions`, there are three scenarios to create data used to compute adjusted predictions:\n",
+    "\n",
+    "1. user provided values \n",
+    "2. a grid of equally spaced and central values\n",
+    "3. empirical distribution (original data used to fit the model)\n",
+    "\n",
+    "In the case of (1) above, a dictionary is passed with the explanatory variables as keys, and the values to condition on are the values. With this dictionary, Bambi assembles all pairwise combinations (transitions) of the specified explanatory variables into a new \"hypothetical\" dataset. Covariates not existient in the dictionary are held at their mean or mode. \n",
+    "\n",
+    "In (2), a string or list is passed with the name(s) of the explanatory variable(s) to create a grid of equally spaced values. This is done by holding all other explanatory variables constant at some specified value, a _reference grid_, that may or may not correspond to actual observations in the dataset used to fit the model. By default, the `plot_predictions` function uses a grid of 200 equally spaced values between the minimum and maximum values of the specified explanatory variable as the reference grid.\n",
+    "\n",
+    "Lastly, in (3), the original data used to fit the model is used compute predictions. This is known as _unit-level_ predictions.\n",
+    "\n",
+    "Using the data, from scenario 1, 2, or 3, the `plot_predictions` function uses the fitted model to then compute the predictions. The `plot_predictions` function then uses these predictions to plot the model parameter as a function of (some) explanatory variable. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import warnings\n",
+    "\n",
+    "import bambi as bmb\n",
+    "\n",
+    "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gaussian Linear Model\n",
+    "\n",
+    "For the first demonstration, we will use a Gaussian linear regression model with the `mtcars` dataset to better understand the `plot_predictions` function and its arguments. The `mtcars` dataset was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973--74 models). The following is a brief description of the variables in the dataset:\n",
+    "\n",
+    "- mpg: Miles/(US) gallon\n",
+    "- cyl: Number of cylinders\n",
+    "- disp: Displacement (cu.in.)\n",
+    "- hp: Gross horsepower\n",
+    "- drat: Rear axle ratio\n",
+    "- wt: Weight (1000 lbs)\n",
+    "- qsec: 1/4 mile time\n",
+    "- vs: Engine (0 = V-shaped, 1 = straight)\n",
+    "- am: Transmission (0 = automatic, 1 = manual)\n",
+    "- gear: Number of forward gear"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [mpg_sigma, hp, wt, hp:wt, cyl, gear]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
        "\n",
-       "This notebook shows how to use, and the capabilities, of the `plot_predictions` function. The `plot_predictions` function is a part of Bambi's sub-package `interpret` that features a set of tools used to interpret complex regression models that is inspired by the R package [marginaleffects](https://vincentarelbundock.github.io/marginaleffects/articles/predictions.html#conditional-adjusted-predictions-plot). "
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
       ]
      },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "## Interpreting Generalized Linear Models\n",
-       "\n",
-       "The purpose of the _generalized linear model_ (GLM) is to unify the approaches needed to analyze data for which either: (1) the assumption of a linear relation between $x$ and $y$, or (2) the assumption of normal variation is not appropriate. GLMs are typically specified in three stages:\n",
-       "1. the linear predictor $\\eta = X\\beta$ where $X$ is an $n$ x $p$ matrix of explanatory variables.\n",
-       "2. the link function $g(\\cdot)$ that relates the linear predictor to the mean of the outcome variable $\\mu = g^{-1}(\\eta) = g^{-1}(X\\beta)$\n",
-       "3. the random component specifying the distribution of the outcome variable $y$ with mean $\\mathbb{E}(y|X) = \\mu$.\n",
-       "\n",
-       "Based on these three specifications, the mean of the distribution of $y$, given $X$, is determined by $X\\beta: \\mathbb{E}(y|X) = g^{-1}(X\\beta)$. \n",
-       "\n",
-       "GLMs are a broad family of models where the output $y$ is typically assumed to follow an exponential family distribution, e.g., Binomial, Poisson, Gamma, Exponential, and Normal. The job of the link function is to map the linear space of the model $X\\beta$ onto the non-linear space of a parameter like $\\mu$. Commonly used link function are the _logit_ and _log_ link. Also known as the _canonical_ link functions. This brief introduction to GLMs is not meant to be exhuastive, and another good starting point is the Bambi [Basic Building Blocks](https://bambinos.github.io/bambi/notebooks/how_bambi_works.html#Link-functions) example.\n",
-       "\n",
-       "Due to the link function, there are typically three quantities of interest to interpret in a GLM:\n",
-       "1. the linear predictor $\\eta$\n",
-       "2. the mean $\\mu = g^{-1}(\\eta)$\n",
-       "3. the response variable $Y \\sim \\mathcal{D}(\\mu, \\theta)$ where $\\mu$ is the mean parameter and $\\theta$ is (possibly) a vector that contains all the other \"nuissance\" parameters of the distribution.\n",
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
        "\n",
-       "As modelers, we are usually more interested in interpreting (2) and (3). However, $\\mu$ is not always on the same scale of the response variable and can be more difficult to interpret. Rather, the response scale is a more interpretable scale. Additionally, it is often the case that modelers would like to analyze how a model parameter varies across a range of explanatory variable values. To achieve such an analysis, Bambi has taken inspiration from the R package marginaleffects, and implemented a `plot_predictions` function that plots the conditional adjusted predictions to aid in the interpretation of GLMs. Below, it is briefly discussed what are conditionally adjusted predictions, how they are computed, and ultimately how to use the `plot_predictions` function."
+       "    <div>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:17&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
       ]
      },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "## Conditionally Adjusted Predictions\n",
-       "\n",
-       "Adjusted predictions refers to the outcome predicted by a fitted model on a specified scale for a given combination of values of the predictor variables, such as their observed values, their means, or some user specified grid of values. The specification of the scale to make the predictions, the link or response scale, refers to the scale used to estimate the model. In normal linear regression, the link scale and the response scale are identical, and therefore, the adjusted prediction is expressed as the mean value of the response variable at the given values of the predictor variables. On the other hand, a logistic regression's link and response scale are not identical. An adjusted prediction on the link scale will be represented as the log-odds of a successful response given values of the predictor variables. Whereas an adjusted prediction on the response scale gives the probability that the response variable equals 1. The conditional part of conditionally adjusted predictions represents the specific predictor(s) and its values we would like to condition on when plotting predictions."
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 18 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load data\n",
+    "data = bmb.load_data('mtcars')\n",
+    "data[\"cyl\"] = data[\"cyl\"].replace({4: \"low\", 6: \"medium\", 8: \"high\"})\n",
+    "data[\"gear\"] = data[\"gear\"].replace({3: \"A\", 4: \"B\", 5: \"C\"})\n",
+    "data[\"cyl\"] = pd.Categorical(data[\"cyl\"], categories=[\"low\", \"medium\", \"high\"], ordered=True)\n",
+    "\n",
+    "# Define and fit the Bambi model\n",
+    "model = bmb.Model(\"mpg ~ 0 + hp * wt + cyl + gear\", data)\n",
+    "idata = model.fit(draws=1000, target_accept=0.95, random_seed=1234)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can print the Bambi model object to obtain the model components. Below, we see that the Gaussian linear model uses an identity link function that results in no transformation of the linear predictor to the mean of the outcome variable, and the distrbution of the likelihood is Gaussian."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Default values\n",
+    "\n",
+    "Now that we have fitted the model, we can visualize how a model parameter varies as a function of (some) interpolated covariate. For this example, we will visualize how the mean response `mpg` varies as a function of the covariate `hp`. \n",
+    "\n",
+    "The Bambi model, ArviZ inference data object (containing the posterior samples and the data used to fit the model), and a list or dictionary of covariates, in this example only `hp`, are passed to the `plot_predictions` function. The `plot_predictions` function then computes the conditional adjusted predictions for each `covariate` in the list or dictionary using the method described above. The `plot_predictions` function returns a `matplotlib` figure object that can be further customized.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 840x360 with 1 Axes>"
       ]
      },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "### Computing Adjusted Predictions\n",
-       "\n",
-       "The objective of plotting conditional adjusted predictions is to visualize how a parameter of the (conditional) response distribution varies as a function of (some) interpolated explanatory variables. This is done by holding all other explanatory variables constant at some specified value, a _reference grid_, that may or may not correspond to actual observations in the dataset used to fit the model. By default, the `plot_predictions` function uses a grid of 200 equally spaced values between the minimum and maximum values of the specified explanatory variable as the reference grid.\n",
-       "\n",
-       "The `plot_predictions` function uses the fitted model to then compute the predicted values of the model parameter at each value of the reference grid. The `plot_predictions` function then uses these predictions to plot the model parameter as a function of (some) explanatory variable. "
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
+    "bmb.interpret.plot_predictions(model, idata, \"hp\", ax=ax);"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The plot above shows that as `hp` increases, the mean `mpg` decreases. As stated above, this insight was obtained by creating the reference grid and then using the fitted model to compute the predicted values of the model parameter, in this example `mpg`, at each value of the reference grid.\n",
+    "\n",
+    "By default, `plot_predictions` uses the highest density interval (HDI) of the posterior distribution to compute the credible interval of the conditional adjusted predictions. The HDI is a Bayesian analog to the frequentist confidence interval. The HDI is the shortest interval that contains a specified probability of the posterior distribution. By default, `plot_predictions` uses the 94% HDI.\n",
+    "\n",
+    "`plot_predictions` uses the posterior distribution by default to visualize some mean outcome parameter . However, the posterior predictive distribution can also be plotted by specifying `pps=True` where `pps` stands for posterior predictive samples of the response variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 840x360 with 1 Axes>"
       ]
      },
-     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-       "import arviz as az\n",
-       "import matplotlib.pyplot as plt\n",
-       "import numpy as np\n",
-       "import pandas as pd\n",
-       "\n",
-       "import bambi as bmb"
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
+    "bmb.interpret.plot_predictions(model, idata, \"hp\", pps=True, ax=ax);"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we notice that the uncertainty in the conditional adjusted predictions is much larger than the uncertainty when `pps=False`. This is because the posterior predictive distribution accounts for the uncertainty in the model parameters and the uncertainty in the data. Whereas, the posterior distribution only accounts for the uncertainty in the model parameters.\n",
+    "\n",
+    "`plot_predictions` allows up to three covariates to be plotted simultaneously where the first element in the list represents the main (x-axis) covariate, the second element the group (hue / color), and the third element the facet (panel). However, when plotting more than one covariate, it can be useful to pass specific `group` and `panel` arguments to aid in the interpretation of the plot. Therefore, `subplot_kwargs` allows the user to manipulate the plotting by passing a dictionary where the keys are `{\"main\": ..., \"group\": ..., \"panel\": ...}` and the values are the names of the covariates to be plotted. For example, passing two covariates `hp` and `wt` and specifying `subplot_kwargs={\"main\": \"hp\", \"group\": \"wt\", \"panel\": \"wt\"}`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x800 with 6 Axes>"
       ]
      },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "## Gaussian Linear Model\n",
-       "\n",
-       "For the first demonstration, we will use a Gaussian linear regression model with the `mtcars` dataset to better understand the `plot_predictions` function and its arguments. The `mtcars` dataset was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973--74 models). The following is a brief description of the variables in the dataset:\n",
-       "\n",
-       "- mpg: Miles/(US) gallon\n",
-       "- cyl: Number of cylinders\n",
-       "- disp: Displacement (cu.in.)\n",
-       "- hp: Gross horsepower\n",
-       "- drat: Rear axle ratio\n",
-       "- wt: Weight (1000 lbs)\n",
-       "- qsec: 1/4 mile time\n",
-       "- vs: Engine (0 = V-shaped, 1 = straight)\n",
-       "- am: Transmission (0 = automatic, 1 = manual)\n",
-       "- gear: Number of forward gear"
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bmb.interpret.plot_predictions(\n",
+    "    model=model, \n",
+    "    idata=idata, \n",
+    "    conditional=[\"hp\", \"wt\"],\n",
+    "    pps=False,\n",
+    "    legend=False,\n",
+    "    subplot_kwargs={\"main\": \"hp\", \"group\": \"wt\", \"panel\": \"wt\"},\n",
+    "    fig_kwargs={\"figsize\": (20, 8), \"sharey\": True}\n",
+    ")\n",
+    "plt.tight_layout();"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Furthermore, categorical covariates can also be plotted. We plot the the mean `mpg` as a function of the two categorical covariates `gear` and `cyl` below. The `plot_predictions` function automatically plots the conditional adjusted predictions for each level of the categorical covariate. Furthermore, when passing a list of covariates into the `plot_predictions` function, the list will be converted into a dictionary object where the key is taken from (\"horizontal\", \"color\", \"panel\") and the values are the names of the variables. By default, the first element of the list is specified as the \"horizontal\" covariate, the second element of the list is specified as the \"color\" covariate, and the third element of the list is mapped to different plot panels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 840x360 with 1 Axes>"
       ]
      },
-     {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {},
-      "outputs": [
-       {
-        "name": "stderr",
-        "output_type": "stream",
-        "text": [
-         "Auto-assigning NUTS sampler...\n",
-         "Initializing NUTS using jitter+adapt_diag...\n",
-         "Multiprocess sampling (4 chains in 4 jobs)\n",
-         "NUTS: [mpg_sigma, hp, wt, hp:wt, cyl, gear]\n"
-        ]
-       },
-       {
-        "data": {
-         "text/html": [
-          "\n",
-          "<style>\n",
-          "    /* Turns off some styling */\n",
-          "    progress {\n",
-          "        /* gets rid of default border in Firefox and Opera. */\n",
-          "        border: none;\n",
-          "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-          "        background-size: auto;\n",
-          "    }\n",
-          "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-          "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
-          "    }\n",
-          "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-          "        background: #F44336;\n",
-          "    }\n",
-          "</style>\n"
-         ],
-         "text/plain": [
-          "<IPython.core.display.HTML object>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       },
-       {
-        "data": {
-         "text/html": [
-          "\n",
-          "    <div>\n",
-          "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-          "      100.00% [8000/8000 00:19&lt;00:00 Sampling 4 chains, 0 divergences]\n",
-          "    </div>\n",
-          "    "
-         ],
-         "text/plain": [
-          "<IPython.core.display.HTML object>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       },
-       {
-        "name": "stderr",
-        "output_type": "stream",
-        "text": [
-         "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 20 seconds.\n"
-        ]
-       }
-      ],
-      "source": [
-       "# Load data\n",
-       "data = bmb.load_data('mtcars')\n",
-       "data[\"cyl\"] = data[\"cyl\"].replace({4: \"low\", 6: \"medium\", 8: \"high\"})\n",
-       "data[\"gear\"] = data[\"gear\"].replace({3: \"A\", 4: \"B\", 5: \"C\"})\n",
-       "data[\"cyl\"] = pd.Categorical(data[\"cyl\"], categories=[\"low\", \"medium\", \"high\"], ordered=True)\n",
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
+    "bmb.interpret.plot_predictions(model, idata, [\"gear\", \"cyl\"], ax=ax);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### User provided values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
        "\n",
-       "# Define and fit the Bambi model\n",
-       "model = bmb.Model(\"mpg ~ 0 + hp * wt + cyl + gear\", data)\n",
-       "idata = model.fit(draws=1000, target_accept=0.95, random_seed=1234)"
-      ]
-     },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "We can print the Bambi model object to obtain the model components. Below, we see that the Gaussian linear model uses an identity link function that results in no transformation of the linear predictor to the mean of the outcome variable, and the distrbution of the likelihood is Gaussian."
-      ]
-     },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "Now that we have fitted the model, we can visualize how a model parameter varies as a function of (some) interpolated covariate. For this example, we will visualize how the mean response `mpg` varies as a function of the covariate `hp`. \n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
        "\n",
-       "The Bambi model, ArviZ inference data object (containing the posterior samples and the data used to fit the model), and a list or dictionary of covariates, in this example only `hp`, are passed to the `plot_predictions` function. The `plot_predictions` function then computes the conditional adjusted predictions for each `covariate` in the list or dictionary using the method described above. The `plot_predictions` function returns a `matplotlib` figure object that can be further customized.  "
-      ]
-     },
-     {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {},
-      "outputs": [
-       {
-        "data": {
-         "image/png": 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",
-         "text/plain": [
-          "<Figure size 840x360 with 1 Axes>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       }
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hp</th>\n",
+       "      <th>wt</th>\n",
+       "      <th>cyl</th>\n",
+       "      <th>gear</th>\n",
+       "      <th>estimate</th>\n",
+       "      <th>lower_3.0%</th>\n",
+       "      <th>upper_97.0%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>100</td>\n",
+       "      <td>3.325</td>\n",
+       "      <td>low</td>\n",
+       "      <td>A</td>\n",
+       "      <td>20.432632</td>\n",
+       "      <td>17.271952</td>\n",
+       "      <td>23.836528</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>100</td>\n",
+       "      <td>3.325</td>\n",
+       "      <td>medium</td>\n",
+       "      <td>A</td>\n",
+       "      <td>18.694081</td>\n",
+       "      <td>16.234618</td>\n",
+       "      <td>21.121714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>100</td>\n",
+       "      <td>3.325</td>\n",
+       "      <td>high</td>\n",
+       "      <td>A</td>\n",
+       "      <td>19.717661</td>\n",
+       "      <td>16.319097</td>\n",
+       "      <td>22.887609</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>120</td>\n",
+       "      <td>3.325</td>\n",
+       "      <td>low</td>\n",
+       "      <td>A</td>\n",
+       "      <td>19.760181</td>\n",
+       "      <td>16.289537</td>\n",
+       "      <td>23.337116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>3.325</td>\n",
+       "      <td>medium</td>\n",
+       "      <td>A</td>\n",
+       "      <td>18.021630</td>\n",
+       "      <td>15.477181</td>\n",
+       "      <td>20.602577</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>120</td>\n",
+       "      <td>3.325</td>\n",
+       "      <td>high</td>\n",
+       "      <td>A</td>\n",
+       "      <td>19.045210</td>\n",
+       "      <td>16.177441</td>\n",
+       "      <td>21.725896</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "source": [
-       "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
-       "bmb.interpret.plot_predictions(model, idata, \"hp\", ax=ax);"
+      "text/plain": [
+       "    hp     wt     cyl gear   estimate  lower_3.0%  upper_97.0%\n",
+       "0  100  3.325     low    A  20.432632   17.271952    23.836528\n",
+       "1  100  3.325  medium    A  18.694081   16.234618    21.121714\n",
+       "2  100  3.325    high    A  19.717661   16.319097    22.887609\n",
+       "3  120  3.325     low    A  19.760181   16.289537    23.337116\n",
+       "4  120  3.325  medium    A  18.021630   15.477181    20.602577\n",
+       "5  120  3.325    high    A  19.045210   16.177441    21.725896"
       ]
      },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "The plot above shows that as `hp` increases, the mean `mpg` decreases. As stated above, this insight was obtained by creating the reference grid and then using the fitted model to compute the predicted values of the model parameter, in this example `mpg`, at each value of the reference grid.\n",
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wt_median = model.data.wt.median()\n",
+    "\n",
+    "summary_df = bmb.interpret.predictions(\n",
+    "    model,\n",
+    "    idata,\n",
+    "    conditional={\n",
+    "        \"hp\": [100, 120],\n",
+    "        \"wt\": wt_median,\n",
+    "        \"cyl\": [\"low\", \"medium\", \"high\"],\n",
+    "    }\n",
+    ")\n",
+    "summary_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Unit level predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
        "\n",
-       "By default, `plot_predictions` uses the highest density interval (HDI) of the posterior distribution to compute the credible interval of the conditional adjusted predictions. The HDI is a Bayesian analog to the frequentist confidence interval. The HDI is the shortest interval that contains a specified probability of the posterior distribution. By default, `plot_predictions` uses the 94% HDI.\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
        "\n",
-       "`plot_predictions` uses the posterior distribution by default to visualize some mean outcome parameter . However, the posterior predictive distribution can also be plotted by specifying `pps=True` where `pps` stands for posterior predictive samples of the response variable."
-      ]
-     },
-     {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {},
-      "outputs": [
-       {
-        "data": {
-         "image/png": 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",
-         "text/plain": [
-          "<Figure size 840x360 with 1 Axes>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       }
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>cyl</th>\n",
+       "      <th>disp</th>\n",
+       "      <th>hp</th>\n",
+       "      <th>drat</th>\n",
+       "      <th>wt</th>\n",
+       "      <th>qsec</th>\n",
+       "      <th>vs</th>\n",
+       "      <th>am</th>\n",
+       "      <th>gear</th>\n",
+       "      <th>carb</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>21.0</td>\n",
+       "      <td>medium</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>110</td>\n",
+       "      <td>3.90</td>\n",
+       "      <td>2.620</td>\n",
+       "      <td>16.46</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>B</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>21.0</td>\n",
+       "      <td>medium</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>110</td>\n",
+       "      <td>3.90</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>17.02</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>B</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>22.8</td>\n",
+       "      <td>low</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>93</td>\n",
+       "      <td>3.85</td>\n",
+       "      <td>2.320</td>\n",
+       "      <td>18.61</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>21.4</td>\n",
+       "      <td>medium</td>\n",
+       "      <td>258.0</td>\n",
+       "      <td>110</td>\n",
+       "      <td>3.08</td>\n",
+       "      <td>3.215</td>\n",
+       "      <td>19.44</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>18.7</td>\n",
+       "      <td>high</td>\n",
+       "      <td>360.0</td>\n",
+       "      <td>175</td>\n",
+       "      <td>3.15</td>\n",
+       "      <td>3.440</td>\n",
+       "      <td>17.02</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "source": [
-       "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
-       "bmb.interpret.plot_predictions(model, idata, \"hp\", pps=True, ax=ax);"
+      "text/plain": [
+       "    mpg     cyl   disp   hp  drat     wt   qsec  vs  am gear  carb\n",
+       "0  21.0  medium  160.0  110  3.90  2.620  16.46   0   1    B     4\n",
+       "1  21.0  medium  160.0  110  3.90  2.875  17.02   0   1    B     4\n",
+       "2  22.8     low  108.0   93  3.85  2.320  18.61   1   1    B     1\n",
+       "3  21.4  medium  258.0  110  3.08  3.215  19.44   1   0    A     1\n",
+       "4  18.7    high  360.0  175  3.15  3.440  17.02   0   0    A     2"
       ]
      },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "Here, we notice that the uncertainty in the conditional adjusted predictions is much larger than the uncertainty when `pps=False`. This is because the posterior predictive distribution accounts for the uncertainty in the model parameters and the uncertainty in the data. Whereas, the posterior distribution only accounts for the uncertainty in the model parameters.\n",
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
        "\n",
-       "`plot_predictions` allows up to three covariates to be plotted simultaneously where the first element in the list represents the main (x-axis) covariate, the second element the group (hue / color), and the third element the facet (panel). However, when plotting more than one covariate, it can be useful to pass specific `group` and `panel` arguments to aid in the interpretation of the plot. Therefore, `subplot_kwargs` allows the user to manipulate the plotting by passing a dictionary where the keys are `{\"main\": ..., \"group\": ..., \"panel\": ...}` and the values are the names of the covariates to be plotted. For example, passing two covariates `hp` and `wt` and specifying `subplot_kwargs={\"main\": \"hp\", \"group\": \"wt\", \"panel\": \"wt\"}`. "
-      ]
-     },
-     {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {},
-      "outputs": [
-       {
-        "data": {
-         "image/png": 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-         "text/plain": [
-          "<Figure size 2000x800 with 6 Axes>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       }
-      ],
-      "source": [
-       "bmb.interpret.plot_predictions(\n",
-       "    model=model, \n",
-       "    idata=idata, \n",
-       "    covariates=[\"hp\", \"wt\"],\n",
-       "    pps=False,\n",
-       "    legend=False,\n",
-       "    subplot_kwargs={\"main\": \"hp\", \"group\": \"wt\", \"panel\": \"wt\"},\n",
-       "    fig_kwargs={\"figsize\": (20, 8), \"sharey\": True}\n",
-       ")\n",
-       "plt.tight_layout();"
-      ]
-     },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "Furthermore, categorical covariates can also be plotted. We plot the the mean `mpg` as a function of the two categorical covariates `gear` and `cyl` below. The `plot_predictions` function automatically plots the conditional adjusted predictions for each level of the categorical covariate. Furthermore, when passing a list of covariates into the `plot_predictions` function, the list will be converted into a dictionary object where the key is taken from (\"horizontal\", \"color\", \"panel\") and the values are the names of the variables. By default, the first element of the list is specified as the \"horizontal\" covariate, the second element of the list is specified as the \"color\" covariate, and the third element of the list is mapped to different plot panels."
-      ]
-     },
-     {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {},
-      "outputs": [
-       {
-        "data": {
-         "image/png": 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",
-         "text/plain": [
-          "<Figure size 840x360 with 1 Axes>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       }
-      ],
-      "source": [
-       "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
-       "bmb.interpret.plot_predictions(model, idata, [\"gear\", \"cyl\"], ax=ax);"
-      ]
-     },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "## Negative Binomial Model\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
        "\n",
-       "Lets move onto a model that uses a distribution that is a member of the exponential distribution family and utilizes a link function. For this, we will implement the Negative binomial model from the students absences [example](https://bambinos.github.io/bambi/notebooks/negative_binomial.html#Negative-binomial-in-GLM). School administrators study the attendance behavior of high school juniors at two schools. Predictors of the number of days of absence include the type of program in which the student is enrolled and a standardized test in math. We have attendance data on 314 high school juniors. The variables of insterest in the dataset are the following:\n",
-       "\n",
-       "- daysabs: The number of days of absence. It is our response variable.\n",
-       "- progr: The type of program. Can be one of ‘General’, ‘Academic’, or ‘Vocational’.\n",
-       "- math: Score in a standardized math test."
-      ]
-     },
-     {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {},
-      "outputs": [
-       {
-        "name": "stderr",
-        "output_type": "stream",
-        "text": [
-         "Auto-assigning NUTS sampler...\n",
-         "Initializing NUTS using jitter+adapt_diag...\n",
-         "Multiprocess sampling (4 chains in 4 jobs)\n",
-         "NUTS: [daysabs_alpha, prog, scale(math), prog:scale(math)]\n"
-        ]
-       },
-       {
-        "data": {
-         "text/html": [
-          "\n",
-          "<style>\n",
-          "    /* Turns off some styling */\n",
-          "    progress {\n",
-          "        /* gets rid of default border in Firefox and Opera. */\n",
-          "        border: none;\n",
-          "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-          "        background-size: auto;\n",
-          "    }\n",
-          "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-          "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
-          "    }\n",
-          "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-          "        background: #F44336;\n",
-          "    }\n",
-          "</style>\n"
-         ],
-         "text/plain": [
-          "<IPython.core.display.HTML object>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       },
-       {
-        "data": {
-         "text/html": [
-          "\n",
-          "    <div>\n",
-          "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-          "      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 0 divergences]\n",
-          "    </div>\n",
-          "    "
-         ],
-         "text/plain": [
-          "<IPython.core.display.HTML object>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       },
-       {
-        "name": "stderr",
-        "output_type": "stream",
-        "text": [
-         "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n"
-        ]
-       }
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cyl</th>\n",
+       "      <th>gear</th>\n",
+       "      <th>hp</th>\n",
+       "      <th>wt</th>\n",
+       "      <th>estimate</th>\n",
+       "      <th>lower_3.0%</th>\n",
+       "      <th>upper_97.0%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>medium</td>\n",
+       "      <td>B</td>\n",
+       "      <td>110</td>\n",
+       "      <td>2.620</td>\n",
+       "      <td>22.233424</td>\n",
+       "      <td>20.051966</td>\n",
+       "      <td>24.476544</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>medium</td>\n",
+       "      <td>B</td>\n",
+       "      <td>110</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>21.320402</td>\n",
+       "      <td>19.196886</td>\n",
+       "      <td>23.344765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>low</td>\n",
+       "      <td>B</td>\n",
+       "      <td>93</td>\n",
+       "      <td>2.320</td>\n",
+       "      <td>25.901435</td>\n",
+       "      <td>24.255648</td>\n",
+       "      <td>27.558330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>medium</td>\n",
+       "      <td>A</td>\n",
+       "      <td>110</td>\n",
+       "      <td>3.215</td>\n",
+       "      <td>18.751708</td>\n",
+       "      <td>16.259737</td>\n",
+       "      <td>21.293185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>high</td>\n",
+       "      <td>A</td>\n",
+       "      <td>175</td>\n",
+       "      <td>3.440</td>\n",
+       "      <td>16.908354</td>\n",
+       "      <td>15.261489</td>\n",
+       "      <td>18.666662</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "source": [
-       "# Load data, define and fit Bambi model\n",
-       "data = pd.read_stata(\"https://stats.idre.ucla.edu/stat/stata/dae/nb_data.dta\")\n",
-       "data[\"prog\"] = data[\"prog\"].map({1: \"General\", 2: \"Academic\", 3: \"Vocational\"})\n",
-       "\n",
-       "model_interaction = bmb.Model(\n",
-       "    \"daysabs ~ 0 + prog + scale(math) + prog:scale(math)\",\n",
-       "    data,\n",
-       "    family=\"negativebinomial\"\n",
-       ")\n",
-       "idata_interaction = model_interaction.fit(\n",
-       "    draws=1000, target_accept=0.95, random_seed=1234, chains=4\n",
-       ")"
+      "text/plain": [
+       "      cyl gear   hp     wt   estimate  lower_3.0%  upper_97.0%\n",
+       "0  medium    B  110  2.620  22.233424   20.051966    24.476544\n",
+       "1  medium    B  110  2.875  21.320402   19.196886    23.344765\n",
+       "2     low    B   93  2.320  25.901435   24.255648    27.558330\n",
+       "3  medium    A  110  3.215  18.751708   16.259737    21.293185\n",
+       "4    high    A  175  3.440  16.908354   15.261489    18.666662"
       ]
      },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "This model utilizes a log link function and a negative binomial distribution for the likelihood. Also note that this model also contains an interaction `prog:sale(math)`."
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summary_df = bmb.interpret.predictions(\n",
+    "    model,\n",
+    "    idata,\n",
+    "    conditional=None\n",
+    ")\n",
+    "summary_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
       ]
      },
-     {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {},
-      "outputs": [
-       {
-        "data": {
-         "text/plain": [
-          "       Formula: daysabs ~ 0 + prog + scale(math) + prog:scale(math)\n",
-          "        Family: negativebinomial\n",
-          "          Link: mu = log\n",
-          "  Observations: 314\n",
-          "        Priors: \n",
-          "    target = mu\n",
-          "        Common-level effects\n",
-          "            prog ~ Normal(mu: [0. 0. 0.], sigma: [5.0102 7.4983 5.2746])\n",
-          "            scale(math) ~ Normal(mu: 0.0, sigma: 2.5)\n",
-          "            prog:scale(math) ~ Normal(mu: [0. 0.], sigma: [6.1735 4.847 ])\n",
-          "        \n",
-          "        Auxiliary parameters\n",
-          "            alpha ~ HalfCauchy(beta: 1.0)\n",
-          "------\n",
-          "* To see a plot of the priors call the .plot_priors() method.\n",
-          "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()"
-         ]
-        },
-        "execution_count": 8,
-        "metadata": {},
-        "output_type": "execute_result"
-       }
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bmb.interpret.plot_predictions(\n",
+    "    model,\n",
+    "    idata,\n",
+    "    conditional=None,\n",
+    "    average_by=[\"gear\", \"cyl\"],\n",
+    "    fig_kwargs={\"figsize\": (7, 3)},\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Negative Binomial Model\n",
+    "\n",
+    "Lets move onto a model that uses a distribution that is a member of the exponential distribution family and utilizes a link function. For this, we will implement the Negative binomial model from the students absences [example](https://bambinos.github.io/bambi/notebooks/negative_binomial.html#Negative-binomial-in-GLM). School administrators study the attendance behavior of high school juniors at two schools. Predictors of the number of days of absence include the type of program in which the student is enrolled and a standardized test in math. We have attendance data on 314 high school juniors. The variables of insterest in the dataset are the following:\n",
+    "\n",
+    "- daysabs: The number of days of absence. It is our response variable.\n",
+    "- progr: The type of program. Can be one of ‘General’, ‘Academic’, or ‘Vocational’.\n",
+    "- math: Score in a standardized math test."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [daysabs_alpha, prog, scale(math), prog:scale(math)]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
       ],
-      "source": [
-       "model_interaction"
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
       ]
      },
-     {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {},
-      "outputs": [
-       {
-        "data": {
-         "image/png": 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wuMUOCwP3tKUowNa+EPoDcXxidj0cJl25h0RERDStMZVVsKLA/aEWtaSkL4gto0pKujwRdHki+P/e68MMlxlLWu1Y3GKDzaAt9/CpDPyxNP5nywCWtjmwoMnKTW2IiIjKhCG7SmhEAXMa1A1KPvMhdYZ7c28IW/qCCCcyUIDCZibPvd+HjnoTlrTYsaTVDruRgXs6ycrAu/sD6PWrs9osKSIiIpp6/O1bhURBwCyXBbNcFqxY2oweX6ywaDIYT0MBsM8bwz5vDH/e1I/2OiMWt6gz3PUWLo6bLobCSTy/qR8nddZhlttS7uEQERFNKwzZVU4UBHTWm9FZb8YFJzSj1x/PBe4g/LE0AKDHH0ePP44XtwygyWbA4hYbFrfa0WjVs5ygxqWzCv62x4cD/jg+NtPJjWyIiIimCEN2DREFAe1OE9qdJpy/pAl9gUSuhjuI4UgKADAQSmAglMDL24dQb9ZhcYsdS1ptaHUYGbhr2AF/HMORfnxsphNtdaZyD4eIiKjmMWTXKEEQ0FpnRGudEecuasRQOIktuUWT/cEEAMAbTeGNXR68scsDu1GLRS02LGmxo7PeBJGBu+Yk0jLe2DkMp1kHu1ELm1GCzaCFzaCF1SCx9R8REVEJMWRPA4IgoNFmQKPNgDMXNMIbSWJrfwibe4Po8ccBqBubbNztxcbdXph1GixstmFRiw2z3RZouZtgTfFFU/BFU0XHRAEw6yVYDRJsRjV450M4S0yIiIjGjyF7Gqq36HHKXDdOmetGMJ7G1twM997hKBQA0VQWb+3z4619fugkEfMbrVjUYsP8RisDV42SFSCcyCCcyKAvkCi6z2aUsKDJhlkuM2e7iYiIjhFD9jRnN2rxidkufGK2C5FkBtv7Q9jaH0LXUAQZWUEqI2NTbxCbeoPQCAJmN5ixqNmOhc1WWNmLe1oIxTP4x14fNvcGMb/JijkNfHeDiIjoaBiyqcCil/CRGU58ZIYTyXQWO4ci2NIXxI6BMJIZuWh792feAzqcJixqsWFRM1sDTgexVBbv7g9gS18I8xotmMd3NoiIiA6LIZsOSa/V4IRWO05otSOTlbFnOIotfSFs6w8hklQ3v9nni2GfL4YXNg+g0aZX67ib2amk1qUyMjb3hrC9P4xZbjMWNNtg4YY3RERERfibkY5K0oiY12jFvEYrLlrWgh5fDFv61LKS/AK6wVASgyEPXtvhgc0gYWGzDQubbZjlNkMSWVpQizKy+s5G11Ck8K6Gw6Qr97CIiIgqAkM2jUvR5jdLmjAYUjuVbOsPoTegdioJJTL4+14f/r7XB72kBvRFzTbMb2J5QS2SFaDbG0O3N4YWhwELm21otBnKPSwiIqKyKnnITiQS6O7uxty5c6HRMFDVMkEQ0GQ3oMluwJkLGhCMp7EtF7h3eyKQFSB50MLJmW6zOsvdZOWsZw3qCyTQF0jAYpAw223GLJcFRh3/HSAioulnQiH7Zz/7GQKBAG677TYAwNtvv43zzz8fPp8PM2bMwGuvvYb29vaSDJQqn92oxfJZ9Vg+qx6JdBY7BsPY2hfCzsGRhZNdQ2p5wbPvA812Qy5w29DiMLCOu4ZEEhm83xPEpgNBNDuMmO02s1afiIimlQkVyz700ENwOByFr2+++WY4nU785Cc/gaIouOuuuyY6PqpSBq0GH2pz4Asf68Ctn16IL508Ax+f6YTNMPJ3XX8wgVe2D+EXr3Xhhy9ux4b3erFjIIx0Vi7jyKmUZAXo9cfxxs5hbHivF+/3BBBOpMs9LCIiokk3oZns/fv3Y8GCBQCAcDiMN954A//93/+N1atXo66uDrfffntJBknVTRJFzG20Ym6jFZ/5UAv6ggls6w9he38Ifbkt3kMJtRfzP/b6oNOImNtowYImtY6bnStqQzwlY0tfCFv6Qmi06THbbUG70wQNN7ghIqIaNKH0kkwmodWqG5Js3LgRsizj7LPPBgDMmDEDAwMDEx8h1RRBENDqMKLVYcTZCxsRiKWwfSCMbf0h7PFEkVUUpLIjYUwA0O40YWGTFfObbWi06llyUAPUbjRJ6Pb5McttxqJmGxfFEhFRTZlQyO7o6MBf/vIXnH766XjmmWewbNky2Gw2AIDH4yl8TnQ4DpOuqI5711AE2/tD2D4QRjydhQJgvy+G/b4Y/mfrIOpMWsxvUhdOznSZIXHnwaqWysjY3h9G12AE85usWNhsg07iNSUiouo3oZB96aWXYt26ddiwYQPef/993HPPPYX73nrrLcybN2/CA6TpwzBqA5ysrGC/L6aWlQyEMRxJAgD8sTT+tseLv+3xQieJmNtgwYImK+Y3cUOUapaRFWzJLZJdmGv3yK3biYiomk0oldx6662QJAn/7//9P1x88cX4xje+Ubhv8+bNuOSSSyY8QJqeNKKAmS4zZrrMuPCEZgxHktg+EMb2gRC6h6OQFXUWdKSspBdtdUbMb7JhQZMVzXZ2K6lG6ayCDw4EsWMgjEUtNsxrtLJmm4iIqpKgKIpS7kFUgi1btmDJkiXYvHkzFi9ePGWv+/ymfgRi7LYwHvFUFruGwtg+EMaOXFnJwWwGCfObrFjQZMNst4UlCFXKqBOxpMWO2W4LRIZtIiIqo/FmxZK8v55IJPDOO+/A6/Wivr4eJ554IgwG7vhGk8Oo02BpmwNL2xyQFQX7vbHCLPdQWC0rCSUyeLPbjze7/ZBys+L50O00cxOcahFPyXiz24+t/SGc0GrHTJeZ71AQEVFVmHDIvu+++3DnnXciFApBURQIggCr1YrbbrsNN9xwQynGSHRYoiBghsuMGS4zzl/SBF80hR0DIewYDGOPJ4qMrCAjK9g1FMGuoQie+6Afbose85usmN9kxYx6M8sRqkA0mcXf9viwtT+EJS12tv4jIqKKN+EdH2+88Uacc845+Od//mc0NTVhYGAAv/vd73DTTTdBq9Xim9/8ZqnGSnRUTrMOn5jtwidmu5DKyNjtiWD7QBg7B8MIxtWyHE8kCU9XEn/tGoY+t3hyfpPax9tm0Jb5O6AjCcUz+H+7vdDt86Oz3oRZLjPqLfpyD4uIiGiMCdVkz549G5/85Cfx6KOPjrnv0ksvxcaNG7F79+4JDXCqsCa7timKgoFQAjsG1FruHl8Mh/rBb3EYMK/RivmNVrQ7TRBZmlDxHCZtYZEse20TEdFkmdKa7L6+PvzLv/zLIe/74he/iKeeemoiT09UMoIgoNluRLPdiNPnNyCWzGBnbvHkrsFIYfFkXyCBvkACr+3wwKjVYG6jBfNzu1WyRWBlCsTSeHd/AO/3BNDsMGKWy4xWh5ELJYmIqKwmlBrmzZuHwcHBQ97X39+POXPmTOTpiSaNSS9hWXsdlrXXQVYUHPDFsGMwjB2DYfQF1K3e4+ksPjgQxAcHghAAtNYZC7PcrXVGznJXGFkBev1x9Prj0EsiZrjMmO02w2HiQlciIpp6EwrZ69atw/XXX48TTzwRS5YsKRz/4IMPsG7dOtx3330THiDRZBMFAR31ZnTUm3HOoiaEE2nsHIxgx2AYXUNhJNIyFAAH/HEc8MfxyvYhmHQazG2wYF6jFXMaLLCylruiJDMyduRaPNaZtGh2GNFkM8Bt1XPBJBERTYlxh+zPfOYzRV9nMhksW7YMixcvLix83LJlC1paWvDII4/g4osvLtlgiaaC1aDFSZ11OKmzrrDz5M5BNbANhNRZ7lgqi/cPBPH+gSCAkVrueQ1Wdr6oMP5YGv5YGlv7QtCIgNuqR6PNgCabAU6zji0BiYhoUow7ZH/wwQdFv5QkSUJ7eztCoRBCoRAAoL29HQCwadOmEg1zrHfffRfr1q3DP/7xDwQCAXR0dOCf//mfceONN8JkMk3a69L0MnrnyfMWNyEYT2PXoNqtpMsTQSItAyiu5TZoRcxxq7PccxutsBs5y10psjIwEExiIJjE+whCJ4losOrRbDegwWbgtSIiopIZd8ju7u6ehGGMz9atW3HyySdj/vz5uP/+++FyufDGG2/gu9/9Lt5++20888wz5R4i1Si7UYuPzHDiIzOcyMoKenKz3DuHRmq5E2kZm/tC2Nyn/tHZaNNjboMVcxstmFFvhlbD3ScrRSojF8qAAMCk06AxV1bisuhgN2o5001ERMelKtslPP7440gkEnjqqacwe/ZsAMCZZ56J/v5+PPjgg/D7/airqyvzKKnWacSRjXDOXazWcu8aimDnYHHHksFQEoMhtS+3VqPOjOdDt9uiZ4irILFUFnuHo9g7HAUAaDUCXBY96i26wq1eYptAIiI6upKFbI/Hg3g8PuZ4R0dHqV6iQKtV39K12+1Fxx0OB0RRhE7HbgI09awGLU7sqMOJHWrHkl5/HDsGw9g1GMYBfxwKgHRWwc7BCHYORoBNgMOoxdxGC+Y2WDHbbYFRxwBXSdJZBf3BBPqDicIxm1GCy6LPfXC2m4iIDm3CIfuuu+7CAw88AK/Xe8j7s9nsRF9ijMsvvxz3338/rrrqKvzwhz+E2+3G66+/jl/+8pe45pprYDabj/j4oaEheDyeomNdXV0lHydNX6IgoN1pQrvThLMXNiKWymC3J4pdg2HsGooUdp8MxNN4s9uPN7v9EAWgrc5UCN2tDiMXUFagUDyDUDyDPZ7i2W6XRQ+XVZ3xZkkQERFNKGQ//PDDuPvuu3HLLbfg9ttvx6233gpFUfBf//VfMBqNuPnmm0s1ziIzZszAxo0bcfHFFxfKRQDgm9/8Ju6///6jPn79+vVYt27dpIyN6FBMOgkntNpxQqsdiqJgKJzErqEIdg2GsXc4ioysQFaA/b4Y9vtieHnbEAxaEbPdFsxpUEO308x3aCrRwbPdgqDW7qt13epsN1s8EhFNPxPaVv2kk07CJZdcgptvvhlarRZvvfUWTjzxRMTjcZx66qn47Gc/i29961ulHC8AdfHlOeecg8bGRlx//fVwu934+9//jrvuugv/9E//hF//+tdHfPzhZrJXrVrFbdVpyqWzMrqHo4V67qFw8pDn1Zt1ucBtwSy3hVuIVxGjTizMdrutetSZdHyXgoioykzptupdXV1Yvnw5RFF9azSVSgEAjEYjbrjhBtx2222TErJvueUWhEIhvPfee4XSkFNPPRUulwtXXHEFLrvsMpx22mmHfXxDQwMaGhpKPi6i46HViJiba/d34QnNCMbT2D0Uwa6hMLqGIoim1JIrbzQF714f/r7XB1EA2utMhdDdWsfe3JUsnpLR44ujx6euWxFzs911Zh3qzTrUmXUM3kRENWZCIVuS1IcLggCbzYYDBw4U7nO5XOjt7Z3Y6A7jvffew6JFi8bUXn/0ox8FAGzevPmIIZuoktmNWpzYWYcTO9UFlAPBBLpyobvbG0M2V1qyzxfDPl8ML28fgl4SMStXWjLHbYHLwk1WKpmsjGySk6/tFgXAZtTCadbBmQvddSYtJNZ3ExFVpQmF7Llz56KnpweAGnB/9atf4aKLLoIoinjwwQcxY8aMUoxxjJaWFmzevBmRSAQWi6VwfOPGjQCAtra2SXldoqkmCgJaHEa0OIw4dZ4bqYyMbm+0ELoHQ2ppSTIjY1t/CNv61d7cdqMWc9wWzG5Qg7dFX5XdOqcVWQECsTQCo4J3vr67rc6ImS4za7uJiKrIhH7zXnjhhXjjjTdw+eWX49vf/jbOO+88OBwOSJKESCSChx9+uFTjLHLddddh1apVOOecc3D99dfD5XLhb3/7G37wgx9g0aJFuOCCCybldYnKTSeJ6vbtjVYAzQjF0+jyRLB7KIKuoQjCyQwAIBhP4+39fry93w8AaLIZ1FnuBnVDHJ3E2dFqoIwK3pt7Q2iw6jHLbUaH08QZbiKiCjehhY8He/PNN/HEE09AFEV8+tOfxhlnnFGqpx7j1Vdfxd13340PPvgAwWAQ7e3tWLlyJb797W+jvr5+3M833mL2UuHCRyqVfNeSrlzg3jscRSorjzlPIwrocJow223GHDfruauRVqNew1luC9xWfbmHQ0Q0LYw3K5Y0ZFczhmyqNRlZXWynhu6RDXEOppdEzHSZMTtXXtJo5S6U1cRmlDDLZcEst5kdZ4iIJtGUdhdJJBJIpVKw2WyFY3/4wx/wzjvv4JxzzsFZZ501kacnogmQRDU8z3SZcc6iRsRTWewdjqDLE8XuoQg8kZF67u0DYWwfCAMALHoJs3Kz3LMbLKgzsT93JQvFM3ivJ4APDgTQ7DBilsuMZruB5SRERGU2oZD9xS9+EWazGY888ggA4IEHHsB1110HAPjxj3+MZ599FhdeeOFEx0hEJWDUabCoxY5FLXYAat32Hk8Euz1qeUkoodZzR5IZfHAgiA8OBAEATrMOs91mzHJbMIuL7yqWrAC9/jh6/fHCgsk6k25UtxJ2KiEimkoTCtn/+Mc/8MMf/rDw9QMPPIBLL70UP//5z/Gv//qvuOeeexiyiSqU3ajFhzvq8OGOOiiKguFICrtzoXuPJ4p4Wu3P7Yum4Ium8Ga3uoiy0abHLLcFs10WzHSZYdSxRKHSjF4wuXe4uFNJnUmHegtbBBIRTbYJhWyPx4PW1lYAwN69e7Fnzx488cQTsNls+Nd//VdcdtllJRkkEU0uQRDgtqq7ES6fVQ9ZUdAXiGO3J4o9ngi6vVGks2pF92AoicFQEht3eyEAaHEYCzPd7FxSuQ4VvPO9uevNOtTntoC3G7WsySciKoEJhWyTyYRgUH1L+S9/+QssFgs+8pGPAAAMBgMikcjER0hEU04UBLTVmdBWZ8Jp89zIZGX0+OO58pIoenwxZBUFCoDeQBy9gTje2DVc2IlyVi50dzhN0HKmtGKN7s29O9ebW9IIhdBdb9bBbdVzQSUR0XGYUMg+4YQT8Itf/AKdnZ1Yv349zjjjjMIMyP79+9HU1FSSQRJReUmakUWUZy0EUhkZ+3xR7PFEsdsTQW+uc8nonShf3eEptAuc5TJjptuMjjr2d650maxSeLciz6zXwGXRo96iQ71ZD7tRy3csiIiOYkIh+7bbbsOKFSuwbNky6HQ6vPTSS4X7/vznP+PEE0+c8ACJqPLoJBFzG6yY22AFACTSWewdjhZmugdCCQBAVlawdziqlidsByRRQEe9CbNcFsx2m9FaZ4QkMqxVumgyi2gyhn3eWOGYIABajQidJEKnEaGXxJGvc8d0knrcpNOgzqSDyH7sRDSNTChkn3nmmdi2bRvefvttLFu2DLNmzSq6b9myZRMdHxFVAYNWg4XNNixsVtt5RpMZNXQPq4soh8LqrGhGVrDHo86Av7RN3VSls96sznS7GLqriaKo72ikMmM3PDoUSRTgNOvgsqq13y4Ly1CIqLZNKGSnUil0dnais7NzzH1f/epXJ/LURFTFzHoJS1rtWNKqtgsMJ9K5me4o9gxHMZzr0Z3OKoUdKgGG7lqWkdUdSfN/cAGA1SDBbdXDZdHDbdHDbmJ7SCKqHRMK2a2trfjyl7+Mq666Ch0dHaUaExHVGKtBi6VtDixtcwAAQvE09uTKS/YMR+GLpgAcJnQ7zZjhUoN3W52RNd01JJzIIJzIYE9u0aVOElFv0cFtUTvd1Jt1vN5EVLUmFLJXrlyJBx54APfccw9WrFiBr3/969zlkYiOymbUYlm7A8vaHQDUjXHU2m21vMQ7OnR7IujyjITudqepsAizvY7dS2pJKiOjP5BAf0Ct6RcFoC7X4SQfvFliQkTVYkIh++GHH8a9996LX/3qV/jP//xPnHvuuZg3bx6uueYaXH755bBaraUaJxHVMPtBoTuUC917cosmR5eX5Gu6AUAjCmivM2KmS53t7nSyT3ctkRXAG0nBG0lhO8IAAItBKgRut1XtdEJEVIkERVGUUjyRoih49tln8fOf/xwvv/wyzGYzLrvsMnz961/HggULSvESk2rLli1YsmQJNm/ejMWLF0/Z6z6/qR+BWHrKXo+oGoUS6UKXkr2eKDyR5CHPEwWg1TESumfUmznzWeP0kginWQeLQYJJp4FFL8Gsl2DRS7z2RFRS482KJQvZee+++y5uuOEGvPbaa+oLCAJWrVqF//iP/0BDQ0MpX6qkGLKJqkck171k73AU3cMjLQMPJgBothsKgXuGywyLfkJv4FEVkUQBJr2mELrNutxt7hhDOBGNx3izYkl+22QyGTz55JP4xS9+gY0bN6K9vR0//OEP8bnPfQ7PPPMM7rzzTlx22WV48cUXS/FyRDTNWfQSTmi144Rc95JYKoN93lghePcF1M1xFAB9wQT6ggn8v91eAIDLoseMerWue0a9GQ4TtxGvVRlZQSieQSieOeT9Wo0Aq0GCRa+FxaAGcJtBys2K848xIpqYCf0r0tvbi1/+8pf41a9+hcHBQZxyyin4wx/+gIsvvhhiru3WN77xDbS2tuLSSy8tyYCJiA5m0klFfboT6Sz2+0ZCd68/jmzuTbvhSBLDkSTe2ucHoNaDz6g3FWa7G6x6hu5pIp1V4Ium4YuOfTdRI6IwA241SNCIIkQBEAUBGlHI3RZ/LYoCNIIArUZAvUVfhu+IiCrJhEL2jBkzIEkSPv/5z+Paa6897OYzs2bNQmNj40ReiojomBm0GsxrtGJeo7r4OpWRccAfw15vFPuGY9jviyGVVTdRCcbTeP9AEO8fCAIATDoNOuvNmFFvQme9GS0OA3t1T0NZGUecBT8aq0HCnAYLZrnN0EssSyGajiYUsteuXYuvfvWrcLvdRzxv2bJl2Lt370ReiojouOkkEbPcFsxyWwCo2733BeLo9kbR7Y2heziKeDoLAIilstjWH8K2/hAAtaSgrc6kznbXm9HuNLGWl44qnMjg3f0BfHAggA6nGXMbLXBxdptoWplQyP7Od75TqnEQEU0Zjaj22253mnDKXEBWFHjCSXUhpTeKfd4YgnG1hCCdVQplJ4CnsJiyM1de0llvgs3ANnJ0aFkZhZ8fp1mLOQ1WzKg3cZMdommgJCs7gsEgdu7ciXg8Pua+U089tRQvQUQ0aURBQKPNgEabActn1QMAArGUOsvtjWKfN4rBkNo2cPRiyo25xZROsw6dThM6crPdbqseIuu66SC+aBr/2OvDu/v9mOU2Y06DlX2+iWrYhEJ2JpPB1772NTz66KPIZrOHPOdwx4mIKpnDpMMyk66wQU4slcF+XwzdwzHs80ZxIBBHVlYXU/qiKfiiKbzbEwAAGLQiOp3qLHdHvYk7U1KRdFbBjoEIdgxE0GjTY7bbArtRC6NOw1IkohoyoZD9k5/8BM8++ywefvhhXHbZZfjFL34BrVaLX/3qVwgGg3jggQdKNU4iorIy6SQsaLJhQZPawSSdldHrjxfKS/b7YoW67kRaxo7BMHYMqrsUagQBLQ4DOuvN6HCa0FlvgpUlJgRgMJQsvEsCqBsqGbQaGLQiDFoNjFoNjDr11nDQ5xqR75YQVbIJhez/+q//wq233oovfOELuOyyy/Dxj38cJ554Ir785S/jvPPOw6uvvopzzz23VGMlIqoYWo2otv1zmQGM1HXv86oz3ft8MfiiKQBAVlHQ44+jxz9SUje6xKTDaUKjzcASE4KsqItvY6ksgCNvVCZpBOglEXpJDeV6SQO9VhxzzKAVYdRqWAdONMUmFLL37NmDD33oQ4We2InEyK5rX/va13DttdfiBz/4wcRGSERUBUbXdX9sphMAEE6ki0J3XyCOXIXJmBITvSSi3WkqzHS317GLCR1ZJqsgk80imjy2sky9JMKs18CkU7egN+mkwtdmvTpDfqge8Yl0Fol0thD+E+ks4rmv46kM4uksNKIIh1ELh0kLh1EHu0kLm0Fiz3ma1iYUss1mM1KpFARBgNPpxL59+3DyyScDAIxGI7xeb0kGOd2IgjpLJmkE6DQiJI0Ibe5zBeov7lA8g0z+tzURVSSrQYslrXYsye1Mme/Xvd8XG1NikszI6BqKoGsoAkDdEr7RZijMdHc6TXCadQwtdNySGRnJjHzIzXcA9XePUaeBWSdBVhTEc+E611L+KGREEhkcGPVujUYEbAYt7Lng7TCpIZy7adJ0MaGf9AULFhT6X5988sm47777cMopp0Cn0+FHP/oR5s+fX5JB1rKPz3RCEISRQC0Kx/yWXiylbpQQSqQRiqdzt5nc24zjl9/NjOGdaHIc3K9bVhQMh5Nq6PbFsN8bgycy0sVkIJTAQCiBf+z1AVA3yunIzXZ3OE1oqzNBJ7EEgEpDVoBo8thnxo8mKwP+WBr+WBpArHBcJ4moN+vQYNOj0WZAPf94pBo1oZD9uc99Djt37gQArFu3Dqeeeio6OzsBAFqtFk8//fTER1jjJrL1rvqWn4Qmu6HoeDorI5zIIBRPI5rKQBLVWfF8gJfEgz7XCNCKIsTcIppURkY8lUUsncm9HZh/mzBT+DyZOaapDSI6AlEQ0GAzoMFmwEdmqCUmsWRmJHT7YjjgjyGdVf/wjaWy2D4QxvaBcO7xQNOo2e4Opxl1Ji0DC1W0VEZGfzCB/mACQBBajQC3VV8ot3KadeUeIlFJCIqilGzacv/+/diwYQNEUcQ555xTVTPZW7ZswZIlS7B582YsXry43MOpeFlZydXkZZDKyOpHVh75fPTXo45zkpxofLKygv5gHPtzobvHF8vNDB6aWS+pgbvOiPZ6E9ocnO2m6qKTRDTkQneTzQC7iZ14qDKMNyuWtDCqo6MD3/zmN0v5lFShNKIAi16CRT++H6FMVg3asqJAyd3KigIFgCKj8HnhuIKiRTaJ1KgFN+ksMlmmdqptGlHd1r2tzoSTZ6vHwol0IXTv98XQ648XyryiyUzRtvCioNZ2tztN6KhTd7l0Wfj2PFUude1CvFDfbdCKcJi00Gk00GoEaCUROo0IbW69klYjQicd9LVm5N1ZonIZd8gWRXFc/zhzMxoardQtpNJZeWS1eyofvjOIJLOIJTOIJDOFt9qJaoXVoMXiFjsWt6gLKjOyjP5AYmS22x9DIDfbLSsovDWfr+02ajVodxoLwbutzgSjjp1MqDIl0jIGgsmjn3gQvSQW9Rwfuc19SCO9yBnIaTKMO2TffvvtRSH7N7/5DSKRCFauXImmpib09/fjueeeg9lsxhVXXFHSwRIdTKsRYTeKR9yaOJWREUtlEE1lEU1mch9ZRFOZXJ0568upukmi2v6v3WnCJ3PHQok0Dvhi2O+Lo8dfXNsdT2exczCCnYORwnO4LXq0O41oy812N9kM3OyEqlq+m0owfvRztRoBeq0GOo04qtd4rve4NNKDfOR+bgZERzfukH3HHXcUPr/33nvR1NSEl156CRaLpXA8HA7j7LPPhslkKskgiSZCJ4nQSTo4DvPjmMrIozq0ZBCMq59HkhmUbsUC0dSyGbRY1GLHotxsd1ZWMBhKFOq6e/wxDEdShfM9kSQ8kSTe2R8AoIaOFrs6291Wp946jFxUSbUpnVWQzmbG9RhJFKDLhXFdLogXfz32uF4aXzUAVbcJ1WSvX78eP/7xj4sCNgBYrVbcdNNNuPHGG/Gtb31rQgMkmmw6SYTLoofroE4vWVkp9CQPJdKF8J3IZJHOKCVpdSgK+T8C1H+ItRoBoiAgqyjIZtXXkBX1NivLyGQVZGWFC0hp3DSigBaHES0OI5bPqgegdjLp8cfU3Sh9MRzwxwt9u9NZBftyXU7yLHoJ7XX54K2Gb26YQ9NVRlaQKezOeey0GmFU6NaM+h2g3uo0ahkLe4pXvwldvd7eXkjSoZ9CkiQMDAxM5OmJykojCnCYdHCYDt1OSlEUpLMKMrKMdEZBOheC01kZ6ayMjKx+LkAY8w9o/uvjrVFXCsFbQSieLvRYZmtFGg+TXsL8JhvmN9kAqD9X3kgqF7xj6PHF0R8c2aUyksxg20AY23ItBAG1zKStbmTGu8lugCSymwnR4aiz5vl+5IfvFASoiz7rTDrUmXVwmnSoM2thNbDbSrWYUMheuHAh7rvvPlxwwQXQakcueiqVwr333osFCxZMeIBElUoQBOgkATqIwBS3dRUEIbeKHjBoNWiwGXBSRx0GQgns8xbX3xIdK0EQ4LLq4bLq8eGOOgDq4uK+QHzUbHdxC8F8mUl+e3iNKKDFbijMdLc7TdxshOg4JdKje4qrtBoBTrM6AeTMhW+bkVvYV6IJhey77roLq1atwqxZs7B69Wo0NTVhYGAATz/9NAYGBrBhw4YSDZOIjkYcVQ6QlZ3oC8SxzxtDXyB+zKUtggBYDVJhC2S7UQu9VkQwloYvmoI/lkYwnjrGbZapFmg1IjrrzeisNxeORZIZHPDHcm3W1BnvfJlJVlbUQD5qe22DVlRDt8OItjojWutMR1ysTESHl84qGAwlMRga6bgiiQKMOg1MOk3uVlI/12pg1qufs7Rr6k0oZH/605/Giy++iFtvvRW/+MUvIMsyBEHAxz72MfzmN7/B2WefXapxEtE4aESh0G0inZXR64+j2xvFQDBReOvfrNfAblSDtMOkg8Oohc2oPeSK+QbryK6iiqIgFM/AH0vBF0shEEvBH02zVGUaseglLGiyYcGoMhNfNIWeXOg+4I8X/XGXSMvoGoqga2ikm4nVIBVmu9scRrTWGVl/SnScMrKCcCKDcOLwizdFAUUB3KA9eFFmrpMKu6eUzIT/RTvrrLNw1llnIRaLwe/3o66ujl1FiCqIViNihsuMGS4zkpksQvEM7Ebtce8CKAgC7CYt7CYtZmBkdjOWysAXTSEQUzuzRHN9ymOpLLu01DhBEFBv0aPeoseydgcAtXf3YCiJntxmOT3+GDzhJPI/CuFE8aY5AOA060aFbhNaHAboJc6+EZWCrEBtX5s8toWakiiMamd46AWaoxfu6yVuAnSwkk0bmEwmhmuiCqeXNHBbJye0qLMjEtrqio/LsoJIKoNIQg3e4WTx59y1szZJoohWhxGtDmPhWDKTRV8gUVRqMrq+2xdNwRdN4YMDQQCAAMBtVRdWtuaCd7PdAG2JN7UiorEysoJM8tgWaI4maQR1Yb8oQiMK0IgCpINvc5208udIGjWYZ+V8B63RtxhzTFYUnLmgcZK+89Lhe3NENKlEUYDNoIXtMCviY6kM+oMJ9AXi6A8mGLprmF7SYKbLjJmukXdAoskMegPxUcE7jkhSfctbATAUTmIoPNK/O79NfKtD3Tintc6IRpueHU2IKkQmqyCTzQLgjt9VHbL/+te/4vvf/z42btyIRCKBtrY2XHbZZbjtttvKPTQiOkYmnYTZbgtmuy2QZQWeSBK9AbWmNxQf3+YQVH3MegnzGq2Y12gFoNZ3B+Np9Abi6PXHcwF8ZGHl6G3i39rnB6CuQWjKBe/W3Kx3I3esJKIyq9qQ/fjjj+OLX/wiPvvZz+LRRx+FxWLB7t270dfXV+6hEdFxEkUBjTYDGm0GnNhRh3Aijb5AAn3BOIZCCXY1mQYEYaQ//eLcbpWKosAfS+OAP1YUvvOLbbOyoh4PxIFu9XkkUUCT3YAWh1rj3cLgTURTrCpDdm9vL77yla/gq1/9KtavX184fsYZZ5RxVERUalaDFvObtJjfZEUmK2MglMBAMAFvNIVgLF2SXTep8gmC2hfYadZhaZsDACDnNs7pDcQKobsvkEAq95dYRlYK5Sf/yD1PPnjna8VbHEY0sNSEiCZJVYbshx56CNFoFDfffHO5h0JEU0TS5Hot16kLrPOtBH2xVK6riXrLTXimB1EQ4Lbq4bbqsaxdXW0rKwqGw8nCrHZvII7+wwTvvHypSYtDnfXOl5pwcSURTVRVhuw33ngDTqcT27dvx0UXXYTNmzfD6XRi9erV+NGPfgSbzXbExw8NDcHj8RQd6+rqmswhE1GJjW4lOHohXTiRRiC3eU6+j3c8xTqT6UAUBDTYDGiwGQo7VsqKAk84ib5RwbsvEC/8MVZUagJ/7nnUxZUtdiNaHOrMd5PdeNxtL4loeqrKkN3b24tYLIY1a9bg29/+Nu6//368+eabWLt2LTZv3oy//OUvR9xedP369Vi3bt0UjpiIporVoIXVoEW7c6SlaFZWEEupPbujyVG36SxiySyiKbYSrFWiMFLnPzp4D4eT6Auq9d19ue42+Rrv0Ysr396vPo8AwGXVo9VhRHOu1rvFboRRxz7eRHRoVRmyZVlGIpHA2rVrccsttwAATj/9dOh0Olx33XV4+eWXj7jb5NVXX401a9YUHevq6sKqVasmc9hEVCYaUSiE78NJZWTEUhlEU1nE8kE8lUEsmc2F8QxYAl4bRs94jy418UVThZnu/G0irQZvBYAnnIQnnMR7PSPPVWfSqoE7F7pbHIYj/pwR0fRRlSG7vr4eu3btwnnnnVd0/IILLsB1112Hd95554ghu6GhAQ0NDZM9TCKqIurOZTo4jrCnVjyVHZkRz93mZ8KjyUwhkFH1EQUBLoseLoseH8otrsx3NVFru+PqzHcggWhypLWkP5aGP5bGlr6RnSutBgktdiOaHQY0241osRtQZ9ZBPMI7rERUe6oyZC9duhR/+9vfxhxXcns3i1wpTkSTwKjTwKjToP4w92dlpTD7rW4pn8ltY5xBNJVBPJXlbHgVGd3V5ITWkXaC4UQGfbnQnW8xGRi1c2U4kcGORBg7BsOFY3pJRLPdgGaHGrqb7exsQlTrqjJkX3LJJXjwwQfxwgsv4MMf/nDh+PPPPw8AWL58ebmGRkTTmOYou1sqilKoB89vLx9JZhDObTOfrwmmyiUIAmxGLWxGLRY0jyyyjyUz6Asm0B8c6WoyHEki/zdVMiOj2xtDtzdWeIxGENBg0xfNejfbDTBoWedNVAuqMmSfe+65WLlyJb773e9ClmUsX74cb731FtatW4cVK1bgU5/6VLmHSEQ0hiAIMOslmPUSDlWwlsrIiOTCdziZLoRwbyTFnuAVzqSXMKfBgjkNlsKxVEbt7d4XiOcWUsYxEEwUrmVWUQoLLLF/5LnqTFo1cDsMaLaptw6j9ogL+omo8lRlyAaA3//+91i3bh0efPBBrFu3Di0tLbj++uuxdu3acg+NiOi46CQRTkktTxgtnZXR44thnzeGgVACCvN2VdBJIjqcJnQc1OlmOJIsBO++oDrrnd82Hhip897aP1LnbdCKhfruZrsRTXYDGqx6SOznTVSxqjZkG41G3H333bj77rvLPRQiokml1YiY5bZgltuCRDqLfd4Yur1ReCOpcg+NxkkjjmopmDumKAqC8XRhtjs/u+2LjlzfRFrG3uEo9g5HC8dEAWiwGtBkN6DZnr81wqKv2l/tRDWF/ycSEVURg1aD+U1WzG+yIpxIY583hr3DUYQTmaM/mCqSIAhwmHRwmHRYOKrOO5HOYuCg4D0YGik3kRVgIJTAQChR1FbQqpfQZB8dvo1wW/TQiCw3IZpKDNlERFXKatBiSasdS1rt8EaS6PbGsN8X5Q6XNcKg1WCGy4wZo3Y0zcoKPJEkBnLBeyAXviOj2gqGkxmEhyLYNRQpHNOIAhqtejV829Tg3WQ3cNabaBLx/y4iohpQb9Gj3qLHiR0OBOPqtvL+WAq+aBr+WIo7WtYIjSioIdlmwLL2kePhRLoQuAdC6uy3J5wstIzMyoq6s2UwUfR8hVlvm6Ew++22srUgUSkwZBMR1ZDRpQejhRNp+KNp+GIp+KMp+KIptgysIfkdTec2WgvHMlkZQ+FkbsY7ngvfCcRSI4ssDzXrLQqA26ovhPnGXAi3s8MJ0bgwZBMRTQP5ENZRP9LpIpbKwB9LwxNOYjCkLrRj55LaIWnEwpbvgLp9vKIoCCczGMiVmgyE1NuhcKIw6y0rwGAoicFQEu8jWHg+g1ZEo23UrHduASf7ehMdGkM2EdE0ZdJJMOkktDqMANS+zmq9r7rAbvQuhlQbBGFkw6R5o2e9ZRmecLIofA+GEgiNWlCbSMvY51VbSY7mMGkLgTsfwl1WHUtOaNpjyCYiIgBqX+dWh7EQuhPpLIZCSQyG1eDFDia1SxLF3I6TxqLjsWSm0MEk/8fXQCiB9Kga/0AsjUAsje0DI9vIiwLgsqgLLfPBu9FmgMOkhciSE5omGLKJiOiQDFoNOupNhRKTWCqDwVASvmgSsVQWsVQW8VQW8XSWZSY1yqSXCj3a82RFgT+awmAogf5QAoPBBAZCSXhHbSMvK8BQOImhcBIYVXKik0Q0WvWFWW/1Qw+LXmK9N9UchmwiIjomJp2EmS4JM0e1lAPUOt94Wg3dsWQWsXSmEMBjqSxC8TQXWdYQURAK3WwWtdgLx9NZuVDfny83GQwlEYyPlB2lMjJ6/HH0+ONFz2nSadBoU3exHB2+TTrGFKpe/OklIqIJEQShUN8Ny9j7FUXt7dwXSKDXHy8KXVQ7tEULLUfEU1k1cIdHSk4GQ8mireRjqeyYHS0BwGaQ0GAzFGa/G3JBnIstqRowZBMR0aQSBAENVgMarAYsa3cgnEijNxBHr7+4lzPVJqNu7KY6+S4n+cA9mJv5HgolkcqOvOsRSmQQSkTQNarFIADYjVo02vRosBoKtw02PfQSwzdVDoZsIiKaUlaDFguatFjQZEMqI6M/qAbuvmACKZaVTAuju5zMbRjpciIrCoKxdCF0D+Y6nngiSWRH/TUWjKcRjKexc7A4fDtMWjTmAnc+gLutDN9UHgzZRERUNjpJRGe9GZ31ZsiyguFIEj3+OA74Y4gms0d/AqopoiCgzqxDnVmHBc22wvGsrMCXW2w5FFZnv4fCCQyHU8gqYzud7BgMFz2vw6gtBO8Gq55lJzQlGLKJiKgiiKKghh+bASd11sEXTaHHF0OPP4ZQnO0DpzONKMBtVWelgZHFlllZgTeSxGA4iaHczPdQKIHhSHEZUiCeRuAQM992o1YN3daRkhO3lQsuqTT4U0RERBXJadbBadbhQ+0OBGNp9Phj6PHF4OcmOZSjGfWHGVqLw/dwJJlrI6jWeh9q5jtfdrLroJpvi15CQy7U52e9G6xsNUjjw5BNREQVz27Swm6yY0mrHeFEGgf8cfT4YhiOpMo9NKpAGlEotAIcM/MdTeZC90gAP7jmO5LMIJLMYM9B3U4MWrFQcuLOBW+3lZvs0KExZBMRUVWxGrRY2KzFwmYb4qksegNxDEeS8EVTCMbT3BiHDksjjnS6GS2/wc5Qrtwkv5GOJ1zc7SSRlrHfF8N+X/HW8lqNAJelOHg3WPWot3B7+emMIZuIiKqWUafBnAYL5jSoDbrTWRn+aArDkRR80RS80SQXUNJRjd5gZ+GoBZeyoiAUT48K3YnCLPjoPt/prIL+YAL9wcRBzwvUmXSF4J2vK3db9DDquOiy1jFkExFRzdBqxJEa3ZxEOgtvNAVvJAlvJAVvNMVWgXRMREGAw6SDw6TDvMaRVoOKoiCaymIonIBn1Ky3J1y8w6WsQP3Zi6awbaC444lVL8E1KnTnA7jdyNKTWsGQTURENc2g1aDVYURrbidCRVHbwfUH1R0ID+5EQXQ0giDAopdg0Vswy1W8zWkynYUnUhy8h8JJ+KLFP2fhZAbhZGbMLpdajQC3Ra8G8NG3Fj10EktPqglDNhERTSvCqNKAJa12pLMyBkNq4O4PJhBOsF0gHT+9VoO2OhPa6kxFxzOyDF80heF8+B4VxJOj3llJZxX0BRPoO6j0BFD7fR8cvt1WPWwGdj2pRAzZREQ0rWk1YlEoiiQzGAjG0R9UNz1haQmVgiSKh1x0md9i3jMqfHvCSQyHkwjEi9tV5vt9H7zNvE4S4bLo4MrNeOdDuMusg54b7pQNQzYREdEoFr2EOQ1WzGmwFkpL/DG1n3Io11c5luJiSiqN0VvMz3YXl56kMjKGI0kMR0YC+HDuNp1Vis7rCyTQFxg7+20zSGr4tuYDuBrG68w61n5PMoZsIiKiwxhdWjJaKiMXNjIJxlOFz+MpznpT6egkES0OI1py6wny8l1PhiMpeMIJeCIpNYwfYvY7lMgglBjb81sjCnCa87PfI7PgLouOm+6UCEM2ERHROOkkcdQ23yOSmSx80RQ294bgCSfLNDqqdaO7nuTbV+alMjK80WQugCeLZsJH135nZaVQonIwvSQWh2+rHi6z2vfbwPKTY8aQTUREVCJ6SYNmuxHNdiN6fDG81xPgQkqaUjpJLPwMjqYoCiLJDIYj6uLLfPjO95Qfvd18MiOjNxBHbyA+5vktegn1+fBt1qE+NwNeb9FBq2H3k9EYsomIiCZBu9OEVocRu4Yi2NwbLJpFJJpqgiDAatDCatBipstcdF9WVhCIpQqhe3QADx5UfpLfcn6ft3jXSwCwG7VjAni9RQeneXrufMmQTURENElEUcD8JitmuszY0hfEzsEwsszaVGE04sjag/kH3Te6/MR7UAg/eAFwfm3CHk9x/bcAwGHSFma863OlJy6zugBTI9Zm/TdDNhER0STTSSI+3FGHeY1WvN8TQPchZgGJKtHhyk8AIJ7K5gL4SPj25m5Hv3OjAPDH0vDH0tg1VPwcogA4TDq4LDo4zWodeL1ZDeIOs7aqZ8AZsomIiKaIWS/h5DkuzG9K4p39AS6OpKpm1GnQphu78U5+2/n8zLc3klS3l48kMRxNFfWelxXAF1XrwoHi/t+jZ8Cd+fITsxrCM1kZUoXXgDNkExERTbF6ix7nLGrk4kiqSSPbzkvorC+u/x69AHN0+FZvU0hlDz0DfrBLP9EJ10GtNSsNQzYREVGZtDtNaHeaEIynC23WhiNJhOIM3VSbjrQAMx/AvZFUcfiOqmUo+RIUvSSi3qwrx/DHhSGbiIiozOxGLezGkR3/Eulsoc7VE07CF01ywSTVvNEBfMYhAni+BCWWylbFZjkM2URERBXGoNWgrW6k1lWWFfhiqcLmId5okrtL0rQyugSlWlTPSImIiKYpURQK214vbFaPqW+r53fzSyEQS0FWjvw8RDR1GLKJiIiq0MELy7KyUqhdzfcx5mw3UfkwZBMREdUAjSigwWpAg9VQOBZNZjAcSWIwlMRAKIEIu5gQTRmGbCIiohpl1kswj5rtjiQzGAgmMBBMYDCU4FbvRJOIIZuIiGiasOglzGmwYE6D2sXEF02poTsUhyfMDiZEpcSQTURENE05zTo4zTosarEhKyvwhNWykmgyA1lRkJUVKIpa7y0r+Q+MuY8z4kRj1UzIfuihh3DllVfCbDYjEokc/QFERERUoBEFNNkNaLIbjn7yQWKpDHp8cez3xbhVPFFOTYTs3t5e3HjjjWhpaUEwGCz3cIiIiKYVk07C/CYr5jdZGbiJcmoiZH/ta1/DqaeeCqfTiT/+8Y/lHg4REdG0NTpwx1NZ7PfFsN8Xw3AkCYV9vGkaqfqQ/dhjj+H111/H1q1b8Z3vfKfcwyEiIqIco05TFLh7/DHs98bgYeCmaaCqQ/bQ0BCuu+463H333WhraxvX4zweT9Gxrq6uUg+PiIiIcow6DeY1WjGv0YpIMoM9ngh2eyLcMIdqVlWH7Kuvvhrz58/HVVddNa7HrV+/HuvWrZukUREREdGRWPQSlrY5cEKrHQf8cez2RNAfTHB2m2pK1Ybsp556Cs8++yzeffddCIIwrsdeffXVWLNmTdGxrq4urFq1qoQjJCIioiMRBAHtThPanSZEkxns8USx2xNBLJUt99CIJqwqQ3YkEsE111yDb3zjG2hpaUEgEAAApFIpAEAgEIBWq4XZbD7k4xsaGtDQ0DBVwyUiIqKjMOslnNBmx5JWG/qCCXQNRdAXiHN2m6pWVYbs4eFhDA4O4t5778W999475v66ujpcdNFF2LBhw9QPjoiIiI6bIAhodRjR6jAillJntw/4YwjG09yRkqpKVYbspqYmvPrqq2OO33333Xj99dfxwgsvwOVylWFkREREVComnYQlrXYsabVDURSEEhkEY2kE4ikEYmkE4mlEEplyD5PokKoyZBsMBpx++uljjj/yyCPQaDSHvI+IiIiqlyAIsBu1sBu16ICpcDydlRGMpxGIpRHMhW9/LI0Ut3qnMqvKkE1EREQEAFqNCJdFD5dFX3Q8lEjDF0nBG01iOJJCIJZiuQlNqZoK2Y888ggeeeSRcg+DiIiIysxm0MJm0GKGS22CIMsKAvE0vJEkvNEUvJEUQok0F1bSpKmpkE1ERER0KKIowGnWwWnWYW7uWDorwxdNwRdNwR9TS01C8TRkBm8qAYZsIiIimpa0GhGNNgMabYbCMVlWEIyn4Y+l4I+lEciF7yRrvGmcGLKJiIiIckRRQJ1Zhzqzruh4LJXJLapMFW7DiQzLTeiwGLKJiIiIjsKkk2DSSWhxGAvHMlm5MNutlpyoHU64wJIAhmwiIiKi4yJpRLiteritI51NFEVBKJ6BL6bWeftz4ZstBacfhmwiIiKiEhEEAXaTFnaTFjNhLhwPJdLwRlLwRkZaCnKBZW1jyCYiIiKaZPmWgjNzLQWzsgJfVO3j7Y2kMBxJIprMlnmUVEoM2URERERTTCMKY0pNEukshnMz3Qf8MYTi3DK+mjFkExEREVUAg1aDtjoT2upMWNbuQDCexgF/DD2+OHzRVLmHR+PEkE1ERERUgexGLexGOxa32BFLZdDji+OAP4ahcJKtA6sAQzYRERFRhTPpJMxvsmJ+kxWJdBa9gTgO+OMYCMbZMrBCMWQTERERVRGDVoPZbgtmuy1IZ2UMBBMYCicwHFFbBrJrSWVgyCYiIiKqUlqNiHanCe1OE4DiriXDYfWWXUvKgyGbiIiIqEYUdS1pUo/FU2rXEk9EbRfoj6aQ4XT3pGPIJiIiIqphRp2maLZblhUE42n4ctvB+6Lq5jis7S4thmwiIiKiaUQUBdSZdagz6zDbrR6TZUXdlTKqznR7oykEY2nOeE8AQzYRERHRNCeKAhwmHRwmHXBQ8PZFU/DHUgjE0vDH0khlOOV9LBiyiYiIiGiMouA9SjSZQSCehj+aD94pRJIZ9u4+CEM2ERERER0zs16CWS+h1WEsHMtkZQTiaQRiaQRiKfhzt+ns9E3eDNlERERENCGSRoTLoofLoi86Hklmima8A/E0IolMmUY5tRiyiYiIiGhSWPQSLHoJ7c6RY+msDH9MXVjpz5ebJDJI1litN0M2EREREU0ZrUZEg9WABquh6LgsK4ins+pHKotEOotYKlt0LJ7KIlUlvQYZsomIiIio7ERRKNR7H4lcJW0FxXIPgIiIiIjoWImiUO4hHBOGbCIiIiKiEmPIJiIiIiIqMYZsIiIiIqISY8gmIiIiIioxhmwiIiIiohJjyCYiIiIiKjGGbCIiIiKiEmPIJiIiIiIqMYZsIiIiIqISY8gmIiIiIiqxI28OP40kk0kAQFdXV5lHQkRERESVJp8R85nxaBiyc3p6egAAq1atKu9AiIiIiKhi9fT04MQTTzzqeYKiKMoUjKfiBQIBvP7662hvb4dery/pc3d1dWHVqlXYsGED5syZU9LnpsrAa1z7eI1rG69v7eM1rn2TfY2TySR6enpw2mmnweFwHPV8zmTnOBwOXHTRRZP6GnPmzMHixYsn9TWovHiNax+vcW3j9a19vMa1bzKv8bHMYOdx4SMRERERUYkxZBMRERERlRhDNhERERFRiTFkTwG32421a9fC7XaXeyg0SXiNax+vcW3j9a19vMa1r9KuMbuLEBERERGVGGeyiYiIiIhKjCGbiIiIiKjEGLKJiIiIiEqMIZuIiIiIqMQYsidRJBLBddddh5aWFhgMBixbtgz//d//Xe5h0XF45ZVXcMUVV2DBggUwm81obW3FRRddhLfffnvMue+88w7OPvtsWCwWOBwOrF69Gnv27CnDqGkiHnroIQiCAIvFMuY+XuPq9de//hUXXngh6urqYDQaMXfuXNx5551F5/D6Vq93330Xq1atQktLC0wmExYsWIDvfve7iMViRefxGle+cDiMm266Ceeeey7cbjcEQcAdd9xxyHPHcz1/9rOfYcGCBdDr9Zg5cybWrVuHdDo9Kd8DQ/YkWr16NX77299i7dq1eOGFF/DRj34UX/jCF/D444+Xe2g0Tv/xH/+B7u5uXHvttXj++efx05/+FENDQ1i+fDleeeWVwnnbt2/H6aefjlQqhT/84Q94+OGHsXPnTpxyyinweDxl/A5oPHp7e3HjjTeipaVlzH28xtXr8ccfx2mnnQa73Y5HH30Uzz//PG6++WaMbrLF61u9tm7dipNPPhnd3d24//778dxzz+Hzn/88vvvd7+ILX/hC4Txe4+rg9Xrx4IMPIplMYtWqVYc9bzzX83vf+x6uvfZarF69Gv/zP/+Dq6++Gt///vdxzTXXTM43odCk+POf/6wAUB5//PGi4+ecc47S0tKiZDKZMo2Mjsfg4OCYY+FwWGlsbFTOOuuswrE1a9YoLpdLCQaDhWPd3d2KVqtVbrrppikZK03cihUrlJUrVyqXX365Yjabi+7jNa5OBw4cUMxms3LVVVcd8Txe3+p16623KgCUrq6uouNf+cpXFACKz+dTFIXXuFrIsqzIsqwoiqJ4PB4FgLJ27dox5x3r9RweHlYMBoPyla98pejx3/ve9xRBEJQtW7aU/HvgTPYk+dOf/gSLxYI1a9YUHf/Sl76Evr4+/P3vfy/TyOh4NDQ0jDlmsViwaNEi9PT0AAAymQyee+45XHLJJbDZbIXzOjs7ccYZZ+BPf/rTlI2Xjt9jjz2G119/HevXrx9zH69x9XrooYcQjUZx8803H/YcXt/qptVqAQB2u73ouMPhgCiK0Ol0vMZVRBAECIJwxHPGcz1ffPFFJBIJfOlLXyp6ji996UtQFAUbNmwo6fgBlotMms2bN2PhwoWQJKno+NKlSwv3U3ULBoN45513sHjxYgDA7t27EY/HC9d4tKVLl6KrqwuJRGKqh0njMDQ0hOuuuw5333032traxtzPa1y93njjDTidTmzfvh3Lli2DJEloaGjA1772NYRCIQC8vtXu8ssvh8PhwFVXXYU9e/YgHA7jueeewy9/+Utcc801MJvNvMY1ZjzXM5+7TjjhhKLzmpub4XK5JiWXMWRPEq/XC6fTOeZ4/pjX653qIVGJXXPNNYhGo7j11lsBjFzTw113RVHg9/undIw0PldffTXmz5+Pq6666pD38xpXr97eXsRiMaxZswaf+9zn8NJLL+Fb3/oWHn30UVx44YVQFIXXt8rNmDEDGzduxObNmzF79mzYbDasXLkSl19+OX76058C4P/DtWY819Pr9UKv18NsNh/y3MnIZdLRT6HjdaS3OY72FghVtttuuw2/+93v8LOf/QwnnXRS0X287tXpqaeewrPPPot33333qNeJ17j6yLKMRCKBtWvX4pZbbgEAnH766dDpdLjuuuvw8ssvw2QyAeD1rVbd3d1YuXIlGhsb8cc//hFutxt///vfcddddyESieDXv/514Vxe49pyrNdzqq87Q/Ykqa+vP+RfRT6fD8Ch/+qi6rBu3Trcdddd+N73voevf/3rheP19fUADv0uhc/ngyAIcDgcUzVMGodIJIJrrrkG3/jGN9DS0oJAIAAASKVSAIBAIACtVstrXMXq6+uxa9cunHfeeUXHL7jgAlx33XV45513cNFFFwHg9a1Wt9xyC0KhEN57773CbOWpp54Kl8uFK664ApdddhmampoA8BrXivH8m1xfX49EIoFYLFb4g3r0uQdPmJUCy0UmyQknnIBt27Yhk8kUHd+0aRMAYMmSJeUYFk3QunXrcMcdd+COO+7Av//7vxfdN3v2bBiNxsI1Hm3Tpk2YM2cODAbDVA2VxmF4eBiDg4O49957UVdXV/h44oknEI1GUVdXh3/5l3/hNa5ih6rZBFBo3yeKIq9vlXvvvfewaNGiMeUAH/3oRwGgUEbCa1w7xnM987XYB587MDCA4eHhScllDNmT5OKLL0YkEsFTTz1VdPy3v/0tWlpa8PGPf7xMI6Pjdeedd+KOO+7Ad77zHaxdu3bM/ZIkYeXKlXj66acRDocLx/fv349XX30Vq1evnsrh0jg0NTXh1VdfHfNx3nnnwWAw4NVXX8Vdd93Fa1zFLrnkEgDACy+8UHT8+eefBwAsX76c17fKtbS0YMuWLYhEIkXHN27cCABoa2vjNa4x47me559/PgwGAx555JGi53jkkUcgCMIRe3Eft5I3BaSCc845R6mrq1MefPBB5ZVXXlGuvPJKBYDy2GOPlXtoNE733HOPAkA5//zzlY0bN475yNu2bZtisViUU089VXn++eeVp59+WlmyZInS0tKiDA0NlfE7oONxqD7ZvMbVa+XKlYper1fuvPNO5f/+7/+UH/zgB4rBYFBWrFhROIfXt3o988wziiAIyvLly5Xf//73yssvv6x873vfUywWi7Jo0SIlmUwqisJrXE2ef/555cknn1QefvhhBYCyZs0a5cknn1SefPJJJRqNKooyvut51113KYIgKP/+7/+uvPbaa8qPf/xjRa/XK1deeeWkjJ8hexKFw2Hlm9/8ptLU1KTodDpl6dKlyhNPPFHuYdFxOO200xQAh/0Y7a233lLOOussxWQyKTabTVm1atWYzRGoOhwqZCsKr3G1isViys0336y0t7crkiQpHR0dyre//W0lkUgUncfrW71eeeUV5dxzz1WampoUo9GozJs3T7nhhhuU4eHhovN4jatDZ2fnYX/v7t27t3DeeK7nT3/6U2XevHmKTqdTOjo6lLVr1yqpVGpSxi8oyqj9ZImIiIiIaMJYk01EREREVGIM2UREREREJcaQTURERERUYgzZREREREQlxpBNRERERFRiDNlERERERCXGkE1EREREVGIM2UREREREJcaQTURERERUYgzZREQ0Lt///vexYcOGMccfeeQRCIKAt956a+oHRURUYRiyiYhoXA4XsomIaARDNhERERFRiTFkExHViDvuuAOCIOCDDz7AmjVrYLfb4XQ68W//9m/IZDLYsWMHzj//fFitVsyYMQM/+tGPCo9NJBK44YYbsGzZssLjPvGJT+CZZ54peg1BEBCNRvHb3/4WgiBAEAScfvrpReeEw2FcddVVcLlcqK+vx+rVq9HX1zcV/wmIiCoGQzYRUY357Gc/iw996EN46qmncOWVV+InP/kJrr/+eqxatQqf/vSn8ac//Qlnnnkmbr75Zjz99NMAgGQyCZ/PhxtvvBEbNmzAE088gU996lNYvXo1Hn300cJzb9y4EUajERdeeCE2btyIjRs3Yv369UWv/+UvfxlarRaPP/44fvSjH+G1117DpZdeOqX/DYiIyk0q9wCIiKi0vvKVr+Df/u3fAABnn302/vd//xc///nP8fTTT+Piiy8GAJx++ul47rnn8Lvf/Q6rV6+G3W7Hb37zm8JzZLNZnHXWWfD7/bj//vtx2WWXAQCWL18OURThdruxfPnyQ77++eefjwceeKDwtc/nw0033YSBgQE0NTVN1rdNRFRROJNNRFRjVqxYUfT1woULIQgCLrjggsIxSZIwZ84c7Nu3r3DsySefxCc/+UlYLBZIkgStVotf//rX2LZt27he/zOf+UzR10uXLgWAotciIqp1DNlERDXG6XQWfa3T6WAymWAwGMYcTyQSAICnn34an/3sZ9Ha2orHHnsMGzduxJtvvokrrriicM6xqq+vL/par9cDAOLx+Hi/FSKiqsVyESIiwmOPPYaZM2fi97//PQRBKBxPJpNlHBURUfXiTDYREUEQBOh0uqKAPTAwMKa7CKDOTHNWmojoyBiyiYgIK1aswI4dO3D11VfjlVdewW9/+1t86lOfQnNz85hzTzjhBLz22mt49tln8dZbb2HHjh1lGDERUWVjuQgREeFLX/oShoaG8J//+Z94+OGHMWvWLNxyyy04cOAA1q1bV3TuT3/6U1xzzTX4/Oc/j1gshtNOOw2vvfZaeQZORFShBEVRlHIPgoiIiIiolrBchIiIiIioxBiyiYiIiIhKjCGbiIiIiKjEGLKJiIiIiEqMIZuIiIiIqMQYsomIiIiISowhm4iIiIioxBiyiYiIiIhKjCGbiIiIiKjEGLKJiIiIiEqMIZuIiIiIqMQYsomIiIiISowhm4iIiIioxP5/+0+4jC6fk3kAAAAASUVORK5CYII=",
-         "text/plain": [
-          "<Figure size 840x360 with 1 Axes>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       }
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
       ],
-      "source": [
-       "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
-       "bmb.interpret.plot_predictions(\n",
-       "    model_interaction, \n",
-       "    idata_interaction, \n",
-       "    \"math\", \n",
-       "    ax=ax, \n",
-       "    pps=False\n",
-       ");"
-      ]
-     },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "The plot above shows that as `math` increases, the mean `daysabs` decreases. However, as the model contains an interaction term, the effect of `math` on `daysabs` depends on the value of `prog`. Therefore, we will use `plot_predictions` to plot the conditional adjusted predictions for each level of `prog`."
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
       ]
      },
-     {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {},
-      "outputs": [
-       {
-        "data": {
-         "image/png": 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",
-         "text/plain": [
-          "<Figure size 840x360 with 1 Axes>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       }
-      ],
-      "source": [
-       "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
-       "bmb.interpret.plot_predictions(\n",
-       "    model_interaction, \n",
-       "    idata_interaction, \n",
-       "    [\"math\", \"prog\"], \n",
-       "    ax=ax, \n",
-       "    pps=False\n",
-       ");"
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load data, define and fit Bambi model\n",
+    "data = pd.read_stata(\"https://stats.idre.ucla.edu/stat/stata/dae/nb_data.dta\")\n",
+    "data[\"prog\"] = data[\"prog\"].map({1: \"General\", 2: \"Academic\", 3: \"Vocational\"})\n",
+    "\n",
+    "model_interaction = bmb.Model(\n",
+    "    \"daysabs ~ 0 + prog + scale(math) + prog:scale(math)\",\n",
+    "    data,\n",
+    "    family=\"negativebinomial\"\n",
+    ")\n",
+    "idata_interaction = model_interaction.fit(\n",
+    "    draws=1000, target_accept=0.95, random_seed=1234, chains=4\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This model utilizes a log link function and a negative binomial distribution for the likelihood. Also note that this model also contains an interaction `prog:sale(math)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "       Formula: daysabs ~ 0 + prog + scale(math) + prog:scale(math)\n",
+       "        Family: negativebinomial\n",
+       "          Link: mu = log\n",
+       "  Observations: 314\n",
+       "        Priors: \n",
+       "    target = mu\n",
+       "        Common-level effects\n",
+       "            prog ~ Normal(mu: [0. 0. 0.], sigma: [5.0102 7.4983 5.2746])\n",
+       "            scale(math) ~ Normal(mu: 0.0, sigma: 2.5)\n",
+       "            prog:scale(math) ~ Normal(mu: [0. 0.], sigma: [6.1735 4.847 ])\n",
+       "        \n",
+       "        Auxiliary parameters\n",
+       "            alpha ~ HalfCauchy(beta: 1.0)\n",
+       "------\n",
+       "* To see a plot of the priors call the .plot_priors() method.\n",
+       "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()"
       ]
      },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "Passing specific `subplot_kwargs` can allow for a more interpretable plot. Especially when the posterior predictive distribution plot results in overlapping credible intervals."
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_interaction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 840x360 with 1 Axes>"
       ]
      },
-     {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {},
-      "outputs": [
-       {
-        "data": {
-         "image/png": 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-         "text/plain": [
-          "<Figure size 1600x500 with 3 Axes>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       }
-      ],
-      "source": [
-       "bmb.interpret.plot_predictions(\n",
-       "    model_interaction, \n",
-       "    idata_interaction, \n",
-       "    covariates=[\"math\", \"prog\"],\n",
-       "    pps=True,\n",
-       "    subplot_kwargs={\"main\": \"math\", \"group\": \"prog\", \"panel\": \"prog\"},\n",
-       "    legend=False,\n",
-       "    fig_kwargs={\"figsize\": (16, 5), \"sharey\": True}\n",
-       ");"
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
+    "bmb.interpret.plot_predictions(\n",
+    "    model_interaction, \n",
+    "    idata_interaction, \n",
+    "    \"math\", \n",
+    "    ax=ax, \n",
+    "    pps=False\n",
+    ");"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The plot above shows that as `math` increases, the mean `daysabs` decreases. However, as the model contains an interaction term, the effect of `math` on `daysabs` depends on the value of `prog`. Therefore, we will use `plot_predictions` to plot the conditional adjusted predictions for each level of `prog`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 840x360 with 1 Axes>"
       ]
      },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "## Logistic Regression\n",
-       "\n",
-       "To further demonstrate the `plot_predictions` function, we will implement a logistic regression model. This example is taken from the marginaleffects `plot_predictions` [documentation](https://vincentarelbundock.github.io/marginaleffects/articles/predictions.html#prediction-type-or-scale). The internet movie database, http://imdb.com/, is a website devoted to collecting movie data supplied by studios and fans. It claims to be the biggest movie database on the web and is run by Amazon. The movies in this dataset were selected for inclusion if they had a known length and had been rated by at least one imdb user. The dataset below contains 28,819 rows and 24 columns. The variables of interest in the dataset are the following:\n",
-       "- title. Title of the movie.\n",
-       "- year. Year of release.\n",
-       "- budget. Total budget (if known) in US dollars\n",
-       "- length. Length in minutes.\n",
-       "- rating. Average IMDB user rating.\n",
-       "- votes. Number of IMDB users who rated this movie.\n",
-       "- r1-10. Multiplying by ten gives percentile (to nearest 10%) of users who rated this movie a 1.\n",
-       "- mpaa. MPAA rating.\n",
-       "- action, animation, comedy, drama, documentary, romance, short. Binary variables represent- ing if movie was classified as belonging to that genre."
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
+    "bmb.interpret.plot_predictions(\n",
+    "    model_interaction, \n",
+    "    idata_interaction, \n",
+    "    [\"math\", \"prog\"], \n",
+    "    ax=ax, \n",
+    "    pps=False\n",
+    ");"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Passing specific `subplot_kwargs` can allow for a more interpretable plot. Especially when the posterior predictive distribution plot results in overlapping credible intervals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x500 with 3 Axes>"
       ]
      },
-     {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {},
-      "outputs": [
-       {
-        "name": "stderr",
-        "output_type": "stream",
-        "text": [
-         "Modeling the probability that certified_fresh==1\n",
-         "Auto-assigning NUTS sampler...\n",
-         "Initializing NUTS using adapt_diag...\n",
-         "Multiprocess sampling (4 chains in 4 jobs)\n",
-         "NUTS: [length, style, length:style]\n"
-        ]
-       },
-       {
-        "data": {
-         "text/html": [
-          "\n",
-          "<style>\n",
-          "    /* Turns off some styling */\n",
-          "    progress {\n",
-          "        /* gets rid of default border in Firefox and Opera. */\n",
-          "        border: none;\n",
-          "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-          "        background-size: auto;\n",
-          "    }\n",
-          "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-          "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
-          "    }\n",
-          "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-          "        background: #F44336;\n",
-          "    }\n",
-          "</style>\n"
-         ],
-         "text/plain": [
-          "<IPython.core.display.HTML object>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       },
-       {
-        "data": {
-         "text/html": [
-          "\n",
-          "    <div>\n",
-          "      <progress value='3485' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-          "      43.56% [3485/8000 04:04&lt;05:16 Sampling 4 chains, 0 divergences]\n",
-          "    </div>\n",
-          "    "
-         ],
-         "text/plain": [
-          "<IPython.core.display.HTML object>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       }
-      ],
-      "source": [
-       "data = pd.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/ggplot2movies/movies.csv\")\n",
-       "\n",
-       "data[\"style\"] = \"Other\"\n",
-       "data.loc[data[\"Action\"] == 1, \"style\"] = \"Action\"\n",
-       "data.loc[data[\"Comedy\"] == 1, \"style\"] = \"Comedy\"\n",
-       "data.loc[data[\"Drama\"] == 1, \"style\"] = \"Drama\"\n",
-       "data[\"certified_fresh\"] = (data[\"rating\"] >= 8) * 1\n",
-       "data = data[data[\"length\"] < 240]\n",
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bmb.interpret.plot_predictions(\n",
+    "    model_interaction, \n",
+    "    idata_interaction, \n",
+    "    conditional=[\"math\", \"prog\"],\n",
+    "    pps=True,\n",
+    "    subplot_kwargs={\"main\": \"math\", \"group\": \"prog\", \"panel\": \"prog\"},\n",
+    "    legend=False,\n",
+    "    fig_kwargs={\"figsize\": (16, 5), \"sharey\": True}\n",
+    ");"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Logistic Regression\n",
+    "\n",
+    "To further demonstrate the `plot_predictions` function, we will implement a logistic regression model. This example is taken from the marginaleffects `plot_predictions` [documentation](https://vincentarelbundock.github.io/marginaleffects/articles/predictions.html#prediction-type-or-scale). The internet movie database, http://imdb.com/, is a website devoted to collecting movie data supplied by studios and fans. It claims to be the biggest movie database on the web and is run by Amazon. The movies in this dataset were selected for inclusion if they had a known length and had been rated by at least one imdb user. The dataset below contains 28,819 rows and 24 columns. The variables of interest in the dataset are the following:\n",
+    "- title. Title of the movie.\n",
+    "- year. Year of release.\n",
+    "- budget. Total budget (if known) in US dollars\n",
+    "- length. Length in minutes.\n",
+    "- rating. Average IMDB user rating.\n",
+    "- votes. Number of IMDB users who rated this movie.\n",
+    "- r1-10. Multiplying by ten gives percentile (to nearest 10%) of users who rated this movie a 1.\n",
+    "- mpaa. MPAA rating.\n",
+    "- action, animation, comedy, drama, documentary, romance, short. Binary variables represent- ing if movie was classified as belonging to that genre."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Modeling the probability that certified_fresh==1\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [length, style, length:style]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
        "\n",
-       "priors = {\"style\": bmb.Prior(\"Normal\", mu=0, sigma=2)}\n",
-       "model = bmb.Model(\"certified_fresh ~ 0 + length * style\", data=data, priors=priors, family=\"bernoulli\")\n",
-       "idata = model.fit(random_seed=1234, target_accept=0.9, init=\"adapt_diag\")"
-      ]
-     },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "The logistic regression model uses a logit link function and a Bernoulli likelihood. Therefore, the link scale is the log-odds of a successful response and the response scale is the probability of a successful response."
-      ]
-     },
-     {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {},
-      "outputs": [
-       {
-        "data": {
-         "text/plain": [
-          "       Formula: certified_fresh ~ 0 + length * style\n",
-          "        Family: bernoulli\n",
-          "          Link: p = logit\n",
-          "  Observations: 58662\n",
-          "        Priors: \n",
-          "    target = p\n",
-          "        Common-level effects\n",
-          "            length ~ Normal(mu: 0.0, sigma: 0.0708)\n",
-          "            style ~ Normal(mu: 0.0, sigma: 2.0)\n",
-          "            length:style ~ Normal(mu: [0. 0. 0.], sigma: [0.0702 0.0509 0.0611])\n",
-          "------\n",
-          "* To see a plot of the priors call the .plot_priors() method.\n",
-          "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()"
-         ]
-        },
-        "execution_count": 13,
-        "metadata": {},
-        "output_type": "execute_result"
-       }
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
       ],
-      "source": [
-       "model"
-      ]
-     },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "Again, by default, the `plot_predictions` function plots the mean outcome on the response scale. Therefore, the plot below shows the probability of a successful response `certified_fresh` as a function of `length`."
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
       ]
      },
-     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [
-       {
-        "data": {
-         "image/png": 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",
-         "text/plain": [
-          "<Figure size 840x360 with 1 Axes>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       }
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='52' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      0.65% [52/8000 00:09&lt;24:20 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
       ],
-      "source": [
-       "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
-       "bmb.interpret.plot_predictions(model, idata, \"length\", ax=ax);"
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
       ]
      },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "Additionally, we can see how the probability of `certified_fresh` varies as a function of categorical covariates. "
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Not enough samples to build a trace.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m/Users/gabestechschulte/Documents/repos/bambi/docs/notebooks/plot_predictions.ipynb Cell 27\u001b[0m line \u001b[0;36m1\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/gabestechschulte/Documents/repos/bambi/docs/notebooks/plot_predictions.ipynb#X35sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m priors \u001b[39m=\u001b[39m {\u001b[39m\"\u001b[39m\u001b[39mstyle\u001b[39m\u001b[39m\"\u001b[39m: bmb\u001b[39m.\u001b[39mPrior(\u001b[39m\"\u001b[39m\u001b[39mNormal\u001b[39m\u001b[39m\"\u001b[39m, mu\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, sigma\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m)}\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/gabestechschulte/Documents/repos/bambi/docs/notebooks/plot_predictions.ipynb#X35sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m model \u001b[39m=\u001b[39m bmb\u001b[39m.\u001b[39mModel(\u001b[39m\"\u001b[39m\u001b[39mcertified_fresh ~ 0 + length * style\u001b[39m\u001b[39m\"\u001b[39m, data\u001b[39m=\u001b[39mdata, priors\u001b[39m=\u001b[39mpriors, family\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mbernoulli\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/gabestechschulte/Documents/repos/bambi/docs/notebooks/plot_predictions.ipynb#X35sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m idata \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mfit(random_seed\u001b[39m=\u001b[39;49m\u001b[39m1234\u001b[39;49m, target_accept\u001b[39m=\u001b[39;49m\u001b[39m0.9\u001b[39;49m, init\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39madapt_diag\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
+      "File \u001b[0;32m~/Documents/repos/bambi/bambi/models.py:333\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, draws, tune, discard_tuned_samples, omit_offsets, include_mean, inference_method, init, n_init, chains, cores, random_seed, **kwargs)\u001b[0m\n\u001b[1;32m    326\u001b[0m     response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcomponents[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mresponse_name]\n\u001b[1;32m    327\u001b[0m     _log\u001b[39m.\u001b[39minfo(\n\u001b[1;32m    328\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mModeling the probability that \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m==\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m,\n\u001b[1;32m    329\u001b[0m         response\u001b[39m.\u001b[39mresponse_term\u001b[39m.\u001b[39mname,\n\u001b[1;32m    330\u001b[0m         \u001b[39mstr\u001b[39m(response\u001b[39m.\u001b[39mresponse_term\u001b[39m.\u001b[39msuccess),\n\u001b[1;32m    331\u001b[0m     )\n\u001b[0;32m--> 333\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbackend\u001b[39m.\u001b[39;49mrun(\n\u001b[1;32m    334\u001b[0m     draws\u001b[39m=\u001b[39;49mdraws,\n\u001b[1;32m    335\u001b[0m     tune\u001b[39m=\u001b[39;49mtune,\n\u001b[1;32m    336\u001b[0m     discard_tuned_samples\u001b[39m=\u001b[39;49mdiscard_tuned_samples,\n\u001b[1;32m    337\u001b[0m     omit_offsets\u001b[39m=\u001b[39;49momit_offsets,\n\u001b[1;32m    338\u001b[0m     include_mean\u001b[39m=\u001b[39;49minclude_mean,\n\u001b[1;32m    339\u001b[0m     inference_method\u001b[39m=\u001b[39;49minference_method,\n\u001b[1;32m    340\u001b[0m     init\u001b[39m=\u001b[39;49minit,\n\u001b[1;32m    341\u001b[0m     n_init\u001b[39m=\u001b[39;49mn_init,\n\u001b[1;32m    342\u001b[0m     chains\u001b[39m=\u001b[39;49mchains,\n\u001b[1;32m    343\u001b[0m     cores\u001b[39m=\u001b[39;49mcores,\n\u001b[1;32m    344\u001b[0m     random_seed\u001b[39m=\u001b[39;49mrandom_seed,\n\u001b[1;32m    345\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs,\n\u001b[1;32m    346\u001b[0m )\n",
+      "File \u001b[0;32m~/Documents/repos/bambi/bambi/backend/pymc.py:96\u001b[0m, in \u001b[0;36mPyMCModel.run\u001b[0;34m(self, draws, tune, discard_tuned_samples, omit_offsets, include_mean, inference_method, init, n_init, chains, cores, random_seed, **kwargs)\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[39m# NOTE: Methods return different types of objects (idata, approximation, and dictionary)\u001b[39;00m\n\u001b[1;32m     95\u001b[0m \u001b[39mif\u001b[39;00m inference_method \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mmcmc\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mnuts_numpyro\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mnuts_blackjax\u001b[39m\u001b[39m\"\u001b[39m]:\n\u001b[0;32m---> 96\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_run_mcmc(\n\u001b[1;32m     97\u001b[0m         draws,\n\u001b[1;32m     98\u001b[0m         tune,\n\u001b[1;32m     99\u001b[0m         discard_tuned_samples,\n\u001b[1;32m    100\u001b[0m         omit_offsets,\n\u001b[1;32m    101\u001b[0m         include_mean,\n\u001b[1;32m    102\u001b[0m         init,\n\u001b[1;32m    103\u001b[0m         n_init,\n\u001b[1;32m    104\u001b[0m         chains,\n\u001b[1;32m    105\u001b[0m         cores,\n\u001b[1;32m    106\u001b[0m         random_seed,\n\u001b[1;32m    107\u001b[0m         inference_method,\n\u001b[1;32m    108\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs,\n\u001b[1;32m    109\u001b[0m     )\n\u001b[1;32m    110\u001b[0m \u001b[39melif\u001b[39;00m inference_method \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mvi\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m    111\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_run_vi(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m~/Documents/repos/bambi/bambi/backend/pymc.py:172\u001b[0m, in \u001b[0;36mPyMCModel._run_mcmc\u001b[0;34m(self, draws, tune, discard_tuned_samples, omit_offsets, include_mean, init, n_init, chains, cores, random_seed, sampler_backend, **kwargs)\u001b[0m\n\u001b[1;32m    170\u001b[0m \u001b[39mif\u001b[39;00m sampler_backend \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mmcmc\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m    171\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 172\u001b[0m         idata \u001b[39m=\u001b[39m pm\u001b[39m.\u001b[39;49msample(\n\u001b[1;32m    173\u001b[0m             draws\u001b[39m=\u001b[39;49mdraws,\n\u001b[1;32m    174\u001b[0m             tune\u001b[39m=\u001b[39;49mtune,\n\u001b[1;32m    175\u001b[0m             discard_tuned_samples\u001b[39m=\u001b[39;49mdiscard_tuned_samples,\n\u001b[1;32m    176\u001b[0m             init\u001b[39m=\u001b[39;49minit,\n\u001b[1;32m    177\u001b[0m             n_init\u001b[39m=\u001b[39;49mn_init,\n\u001b[1;32m    178\u001b[0m             chains\u001b[39m=\u001b[39;49mchains,\n\u001b[1;32m    179\u001b[0m             cores\u001b[39m=\u001b[39;49mcores,\n\u001b[1;32m    180\u001b[0m             random_seed\u001b[39m=\u001b[39;49mrandom_seed,\n\u001b[1;32m    181\u001b[0m             \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs,\n\u001b[1;32m    182\u001b[0m         )\n\u001b[1;32m    183\u001b[0m     \u001b[39mexcept\u001b[39;00m (\u001b[39mRuntimeError\u001b[39;00m, \u001b[39mValueError\u001b[39;00m):\n\u001b[1;32m    184\u001b[0m         \u001b[39mif\u001b[39;00m (\n\u001b[1;32m    185\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mValueError: Mass matrix contains\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m traceback\u001b[39m.\u001b[39mformat_exc()\n\u001b[1;32m    186\u001b[0m             \u001b[39mand\u001b[39;00m init \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mauto\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    187\u001b[0m         ):\n",
+      "File \u001b[0;32m~/miniforge3/envs/bambinos/lib/python3.11/site-packages/pymc/sampling/mcmc.py:789\u001b[0m, in \u001b[0;36msample\u001b[0;34m(draws, tune, chains, cores, random_seed, progressbar, step, nuts_sampler, initvals, init, jitter_max_retries, n_init, trace, discard_tuned_samples, compute_convergence_checks, keep_warning_stat, return_inferencedata, idata_kwargs, nuts_sampler_kwargs, callback, mp_ctx, model, **kwargs)\u001b[0m\n\u001b[1;32m    785\u001b[0m t_sampling \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m t_start\n\u001b[1;32m    787\u001b[0m \u001b[39m# Packaging, validating and returning the result was extracted\u001b[39;00m\n\u001b[1;32m    788\u001b[0m \u001b[39m# into a function to make it easier to test and refactor.\u001b[39;00m\n\u001b[0;32m--> 789\u001b[0m \u001b[39mreturn\u001b[39;00m _sample_return(\n\u001b[1;32m    790\u001b[0m     run\u001b[39m=\u001b[39;49mrun,\n\u001b[1;32m    791\u001b[0m     traces\u001b[39m=\u001b[39;49mtraces,\n\u001b[1;32m    792\u001b[0m     tune\u001b[39m=\u001b[39;49mtune,\n\u001b[1;32m    793\u001b[0m     t_sampling\u001b[39m=\u001b[39;49mt_sampling,\n\u001b[1;32m    794\u001b[0m     discard_tuned_samples\u001b[39m=\u001b[39;49mdiscard_tuned_samples,\n\u001b[1;32m    795\u001b[0m     compute_convergence_checks\u001b[39m=\u001b[39;49mcompute_convergence_checks,\n\u001b[1;32m    796\u001b[0m     return_inferencedata\u001b[39m=\u001b[39;49mreturn_inferencedata,\n\u001b[1;32m    797\u001b[0m     keep_warning_stat\u001b[39m=\u001b[39;49mkeep_warning_stat,\n\u001b[1;32m    798\u001b[0m     idata_kwargs\u001b[39m=\u001b[39;49midata_kwargs \u001b[39mor\u001b[39;49;00m {},\n\u001b[1;32m    799\u001b[0m     model\u001b[39m=\u001b[39;49mmodel,\n\u001b[1;32m    800\u001b[0m )\n",
+      "File \u001b[0;32m~/miniforge3/envs/bambinos/lib/python3.11/site-packages/pymc/sampling/mcmc.py:820\u001b[0m, in \u001b[0;36m_sample_return\u001b[0;34m(run, traces, tune, t_sampling, discard_tuned_samples, compute_convergence_checks, return_inferencedata, keep_warning_stat, idata_kwargs, model)\u001b[0m\n\u001b[1;32m    818\u001b[0m \u001b[39m# Pick and slice chains to keep the maximum number of samples\u001b[39;00m\n\u001b[1;32m    819\u001b[0m \u001b[39mif\u001b[39;00m discard_tuned_samples:\n\u001b[0;32m--> 820\u001b[0m     traces, length \u001b[39m=\u001b[39m _choose_chains(traces, tune)\n\u001b[1;32m    821\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    822\u001b[0m     traces, length \u001b[39m=\u001b[39m _choose_chains(traces, \u001b[39m0\u001b[39m)\n",
+      "File \u001b[0;32m~/miniforge3/envs/bambinos/lib/python3.11/site-packages/pymc/backends/base.py:601\u001b[0m, in \u001b[0;36m_choose_chains\u001b[0;34m(traces, tune)\u001b[0m\n\u001b[1;32m    599\u001b[0m lengths \u001b[39m=\u001b[39m [\u001b[39mmax\u001b[39m(\u001b[39m0\u001b[39m, \u001b[39mlen\u001b[39m(trace) \u001b[39m-\u001b[39m tune) \u001b[39mfor\u001b[39;00m trace \u001b[39min\u001b[39;00m traces]\n\u001b[1;32m    600\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39msum\u001b[39m(lengths):\n\u001b[0;32m--> 601\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mNot enough samples to build a trace.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    603\u001b[0m idxs \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39margsort(lengths)\n\u001b[1;32m    604\u001b[0m l_sort \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(lengths)[idxs]\n",
+      "\u001b[0;31mValueError\u001b[0m: Not enough samples to build a trace."
+     ]
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/ggplot2movies/movies.csv\")\n",
+    "\n",
+    "data[\"style\"] = \"Other\"\n",
+    "data.loc[data[\"Action\"] == 1, \"style\"] = \"Action\"\n",
+    "data.loc[data[\"Comedy\"] == 1, \"style\"] = \"Comedy\"\n",
+    "data.loc[data[\"Drama\"] == 1, \"style\"] = \"Drama\"\n",
+    "data[\"certified_fresh\"] = (data[\"rating\"] >= 8) * 1\n",
+    "data = data[data[\"length\"] < 240]\n",
+    "\n",
+    "priors = {\"style\": bmb.Prior(\"Normal\", mu=0, sigma=2)}\n",
+    "model = bmb.Model(\"certified_fresh ~ 0 + length * style\", data=data, priors=priors, family=\"bernoulli\")\n",
+    "idata = model.fit(random_seed=1234, target_accept=0.9, init=\"adapt_diag\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The logistic regression model uses a logit link function and a Bernoulli likelihood. Therefore, the link scale is the log-odds of a successful response and the response scale is the probability of a successful response."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "       Formula: certified_fresh ~ 0 + length * style\n",
+       "        Family: bernoulli\n",
+       "          Link: p = logit\n",
+       "  Observations: 58662\n",
+       "        Priors: \n",
+       "    target = p\n",
+       "        Common-level effects\n",
+       "            length ~ Normal(mu: 0.0, sigma: 0.0708)\n",
+       "            style ~ Normal(mu: 0.0, sigma: 2.0)\n",
+       "            length:style ~ Normal(mu: [0. 0. 0.], sigma: [0.0702 0.0509 0.0611])\n",
+       "------\n",
+       "* To see a plot of the priors call the .plot_priors() method.\n",
+       "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()"
       ]
      },
-     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [
-       {
-        "data": {
-         "image/png": 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",
-         "text/plain": [
-          "<Figure size 840x360 with 1 Axes>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       }
-      ],
-      "source": [
-       "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
-       "bmb.interpret.plot_predictions(model, idata, \"style\", ax=ax);"
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, by default, the `plot_predictions` function plots the mean outcome on the response scale. Therefore, the plot below shows the probability of a successful response `certified_fresh` as a function of `length`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 840x360 with 1 Axes>"
       ]
      },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "## Plotting other model parameters\n",
-       "\n",
-       "`plot_predictions` also has the argument `target` where `target` determines what parameter of the response distribution is plotted as a function of the explanatory variables. This argument is useful in distributional models, i.e., when the response distribution contains a parameter for location, scale and or shape. The default of this argument is `mean` and passing a parameter into `target` only works when the argument `pps=False` because when `pps=True` the posterior predictive distribution is plotted and thus, can only refer to the outcome variable (instead of any of the parameters of the response distribution). For this example, we will simulate our own dataset."
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
+    "bmb.interpret.plot_predictions(model, idata, \"length\", ax=ax);"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Additionally, we can see how the probability of `certified_fresh` varies as a function of categorical covariates. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 840x360 with 1 Axes>"
       ]
      },
-     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [
-       {
-        "name": "stderr",
-        "output_type": "stream",
-        "text": [
-         "Auto-assigning NUTS sampler...\n",
-         "Initializing NUTS using jitter+adapt_diag...\n",
-         "Multiprocess sampling (4 chains in 4 jobs)\n",
-         "NUTS: [Intercept, x, alpha_Intercept, alpha_x]\n"
-        ]
-       },
-       {
-        "data": {
-         "text/html": [
-          "\n",
-          "<style>\n",
-          "    /* Turns off some styling */\n",
-          "    progress {\n",
-          "        /* gets rid of default border in Firefox and Opera. */\n",
-          "        border: none;\n",
-          "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-          "        background-size: auto;\n",
-          "    }\n",
-          "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-          "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
-          "    }\n",
-          "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-          "        background: #F44336;\n",
-          "    }\n",
-          "</style>\n"
-         ],
-         "text/plain": [
-          "<IPython.core.display.HTML object>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       },
-       {
-        "data": {
-         "text/html": [
-          "\n",
-          "    <div>\n",
-          "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-          "      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 25 divergences]\n",
-          "    </div>\n",
-          "    "
-         ],
-         "text/plain": [
-          "<IPython.core.display.HTML object>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       },
-       {
-        "name": "stderr",
-        "output_type": "stream",
-        "text": [
-         "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n",
-         "There were 25 divergences after tuning. Increase `target_accept` or reparameterize.\n"
-        ]
-       }
-      ],
-      "source": [
-       "rng = np.random.default_rng(121195)\n",
-       "N = 200\n",
-       "a, b = 0.5, 1.1\n",
-       "x = rng.uniform(-1.5, 1.5, N)\n",
-       "shape = np.exp(0.3 + x * 0.5 + rng.normal(scale=0.1, size=N))\n",
-       "y = rng.gamma(shape, np.exp(a + b * x) / shape, N)\n",
-       "data_gamma = pd.DataFrame({\"x\": x, \"y\": y})\n",
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
+    "bmb.interpret.plot_predictions(model, idata, \"style\", ax=ax);"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting other model parameters\n",
+    "\n",
+    "`plot_predictions` also has the argument `target` where `target` determines what parameter of the response distribution is plotted as a function of the explanatory variables. This argument is useful in distributional models, i.e., when the response distribution contains a parameter for location, scale and or shape. The default of this argument is `mean` and passing a parameter into `target` only works when the argument `pps=False` because when `pps=True` the posterior predictive distribution is plotted and thus, can only refer to the outcome variable (instead of any of the parameters of the response distribution). For this example, we will simulate our own dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [Intercept, x, alpha_Intercept, alpha_x]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
        "\n",
-       "formula = bmb.Formula(\"y ~ x\", \"alpha ~ x\")\n",
-       "model = bmb.Model(formula, data_gamma, family=\"gamma\")\n",
-       "idata = model.fit(random_seed=1234)"
-      ]
-     },
-     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [
-       {
-        "data": {
-         "text/plain": [
-          "       Formula: y ~ x\n",
-          "                alpha ~ x\n",
-          "        Family: gamma\n",
-          "          Link: mu = inverse\n",
-          "                alpha = log\n",
-          "  Observations: 200\n",
-          "        Priors: \n",
-          "    target = mu\n",
-          "        Common-level effects\n",
-          "            Intercept ~ Normal(mu: 0.0, sigma: 2.5037)\n",
-          "            x ~ Normal(mu: 0.0, sigma: 2.8025)\n",
-          "    target = alpha\n",
-          "        Common-level effects\n",
-          "            alpha_Intercept ~ Normal(mu: 0.0, sigma: 1.0)\n",
-          "            alpha_x ~ Normal(mu: 0.0, sigma: 1.0)\n",
-          "------\n",
-          "* To see a plot of the priors call the .plot_priors() method.\n",
-          "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()"
-         ]
-        },
-        "execution_count": 17,
-        "metadata": {},
-        "output_type": "execute_result"
-       }
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
       ],
-      "source": [
-       "model"
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
       ]
      },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "The model we defined uses a `gamma` distribution parameterized by `alpha` and `mu` where `alpha` utilizes a log link and `mu` goes through an inverse link. Therefore, we can plot either: (1) the `mu` of the response distribution (which is the default), or (2) `alpha` of the response distribution as a function of the explanatory variable $x$. "
-      ]
-     },
-     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [
-       {
-        "data": {
-         "image/png": 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",
-         "text/plain": [
-          "<Figure size 840x360 with 1 Axes>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       }
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 25 divergences]\n",
+       "    </div>\n",
+       "    "
       ],
-      "source": [
-       "# First, the mean of the response (default)\n",
-       "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
-       "bmb.interpret.plot_predictions(model, idata, \"x\", ax=ax);"
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
       ]
      },
-     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-       "Below, instead of plotting the default target, `target=mean`, we set `target=alpha` to visualize how the model parameter `alpha` varies as a function of the `x` predictor."
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n",
+      "There were 25 divergences after tuning. Increase `target_accept` or reparameterize.\n"
+     ]
+    }
+   ],
+   "source": [
+    "rng = np.random.default_rng(121195)\n",
+    "N = 200\n",
+    "a, b = 0.5, 1.1\n",
+    "x = rng.uniform(-1.5, 1.5, N)\n",
+    "shape = np.exp(0.3 + x * 0.5 + rng.normal(scale=0.1, size=N))\n",
+    "y = rng.gamma(shape, np.exp(a + b * x) / shape, N)\n",
+    "data_gamma = pd.DataFrame({\"x\": x, \"y\": y})\n",
+    "\n",
+    "formula = bmb.Formula(\"y ~ x\", \"alpha ~ x\")\n",
+    "model = bmb.Model(formula, data_gamma, family=\"gamma\")\n",
+    "idata = model.fit(random_seed=1234)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "       Formula: y ~ x\n",
+       "                alpha ~ x\n",
+       "        Family: gamma\n",
+       "          Link: mu = inverse\n",
+       "                alpha = log\n",
+       "  Observations: 200\n",
+       "        Priors: \n",
+       "    target = mu\n",
+       "        Common-level effects\n",
+       "            Intercept ~ Normal(mu: 0.0, sigma: 2.5037)\n",
+       "            x ~ Normal(mu: 0.0, sigma: 2.8025)\n",
+       "    target = alpha\n",
+       "        Common-level effects\n",
+       "            alpha_Intercept ~ Normal(mu: 0.0, sigma: 1.0)\n",
+       "            alpha_x ~ Normal(mu: 0.0, sigma: 1.0)\n",
+       "------\n",
+       "* To see a plot of the priors call the .plot_priors() method.\n",
+       "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()"
       ]
      },
-     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [
-       {
-        "data": {
-         "image/png": 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",
-         "text/plain": [
-          "<Figure size 840x360 with 1 Axes>"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       }
-      ],
-      "source": [
-       "# Second, another param. of the distribution: alpha\n",
-       "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
-       "bmb.interpret.plot_predictions(model, idata, \"x\", target='alpha', ax=ax);"
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model we defined uses a `gamma` distribution parameterized by `alpha` and `mu` where `alpha` utilizes a log link and `mu` goes through an inverse link. Therefore, we can plot either: (1) the `mu` of the response distribution (which is the default), or (2) `alpha` of the response distribution as a function of the explanatory variable $x$. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 840x360 with 1 Axes>"
       ]
      },
-     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [
-       {
-        "name": "stdout",
-        "output_type": "stream",
-        "text": [
-         "Last updated: Wed Aug 16 2023\n",
-         "\n",
-         "Python implementation: CPython\n",
-         "Python version       : 3.11.0\n",
-         "IPython version      : 8.13.2\n",
-         "\n",
-         "pandas    : 2.0.1\n",
-         "matplotlib: 3.7.1\n",
-         "bambi     : 0.10.0.dev0\n",
-         "arviz     : 0.15.1\n",
-         "numpy     : 1.24.2\n",
-         "\n",
-         "Watermark: 2.3.1\n",
-         "\n"
-        ]
-       }
-      ],
-      "source": [
-       "%load_ext watermark\n",
-       "%watermark -n -u -v -iv -w"
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# First, the mean of the response (default)\n",
+    "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
+    "bmb.interpret.plot_predictions(model, idata, \"x\", ax=ax);"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below, instead of plotting the default target, `target=mean`, we set `target=alpha` to visualize how the model parameter `alpha` varies as a function of the `x` predictor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 840x360 with 1 Axes>"
       ]
-     }
-    ],
-    "metadata": {
-     "kernelspec": {
-      "display_name": "bambinos",
-      "language": "python",
-      "name": "python3"
      },
-     "language_info": {
-      "codemirror_mode": {
-       "name": "ipython",
-       "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.11.0"
-     },
-     "orig_nbformat": 4
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-   }
\ No newline at end of file
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Second, another param. of the distribution: alpha\n",
+    "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n",
+    "bmb.interpret.plot_predictions(model, idata, \"x\", target='alpha', ax=ax);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last updated: Wed Aug 16 2023\n",
+      "\n",
+      "Python implementation: CPython\n",
+      "Python version       : 3.11.0\n",
+      "IPython version      : 8.13.2\n",
+      "\n",
+      "pandas    : 2.0.1\n",
+      "matplotlib: 3.7.1\n",
+      "bambi     : 0.10.0.dev0\n",
+      "arviz     : 0.15.1\n",
+      "numpy     : 1.24.2\n",
+      "\n",
+      "Watermark: 2.3.1\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext watermark\n",
+    "%watermark -n -u -v -iv -w"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "bambinos",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/notebooks/zero_inflated_regression.ipynb b/docs/notebooks/zero_inflated_regression.ipynb
index 409616061..e8947e9be 100644
--- a/docs/notebooks/zero_inflated_regression.ipynb
+++ b/docs/notebooks/zero_inflated_regression.ipynb
@@ -399,7 +399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -486,7 +486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -518,7 +518,7 @@
        "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -764,7 +764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -950,7 +950,7 @@
        "psi_child            4022.0    2883.0    1.0  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -974,7 +974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -994,7 +994,7 @@
     "bmb.interpret.plot_predictions(\n",
     "    zip_model,\n",
     "    zip_idata,\n",
-    "    covariates=\"persons\",\n",
+    "    conditional=\"persons\",\n",
     "    ax=ax[0]\n",
     ")\n",
     "ax[0].set_ylabel(\"mu (fish count)\")\n",
@@ -1003,7 +1003,7 @@
     "bmb.interpret.plot_predictions(\n",
     "    zip_model,\n",
     "    zip_idata,\n",
-    "    covariates=\"persons\",\n",
+    "    conditional=\"persons\",\n",
     "    target=\"psi\",\n",
     "    ax=ax[1]\n",
     ")\n",