Repository: bambinos/kulprit (language: python)

Issue:
Add support for more families
These are the families supported in Bambi https://bambinos.github.io/bambi/notebooks/getting_started.html#families we should aim to have parity.

Kulprit currently supports:

* [x] Gaussian
* [x] Poisson
* [x] Binomial
* [x] Bernoulli



Discussion / hints:

Fix patch:
diff --git a/docs/examples/quick-start.ipynb b/docs/examples/quick-start.ipynb
index 936926a..865aec2 100644
--- a/docs/examples/quick-start.ipynb
+++ b/docs/examples/quick-start.ipynb
@@ -90,34 +90,20 @@
      "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: [y_sigma, Intercept, x1, x2]\n"
+      "NUTS: [sigma, Intercept, x1, x2]\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"
-      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "035e8bec5fc74a73852971ff9af2144e",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "Output()"
       ]
      },
      "metadata": {},
@@ -126,15 +112,21 @@
     {
      "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 00:03&lt;00:00 Sampling 4 chains, 0 divergences]\n",
-       "    </div>\n",
-       "    "
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
       ],
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "\n"
       ]
      },
      "metadata": {},
@@ -144,7 +136,7 @@
      "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 4 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.\n"
      ]
     }
    ],
@@ -195,9 +187,9 @@
     {
      "data": {
       "text/plain": [
-       "  0 y ~ 1\n",
-       "  1 y ~ x1\n",
-       "  2 y ~ x1 + x2"
+       "  0 model_size 1, terms ['Intercept']\n",
+       "  1 model_size 2, terms ['Intercept', 'x1']\n",
+       "  2 model_size 3, terms ['Intercept', 'x1', 'x2']"
       ]
      },
      "execution_count": 6,
@@ -226,7 +218,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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5/tlnnzV+37Vrl7777rtSx3p5eWnQoEFGOzU1VWlpaaWOb9SokR555BGj/e233+rnn38udfzdd9+tBx54wGivXLlS2dnZpY4PCwtT+/btjfbHH39c5o6iW7duuu+++yRd+PB1tW3fp08fNWnSRNKFHdz8+fPLHD9gwAD5+PhIko4dO6Zly5aVOX7EiBHGB4zMzEytXbu21LHu7u6Kj4832mlpaaW+t0pSgwYN1K9fP6OdlJRkc7+TP/L399dDDz1ktL/88ksdPXq01PEhISHq2LGj0V6yZEmZHwIiIiLUqlUro321bV/R/UNcXJzq1q0rqXz7h6FDhxofYK5l/7Bhw4ZSx/9x//DDDz/oxx9/LHV8RfcPrVq1UkREhNG+2v6hY8eOCgkJMdoV2T+cP3++QvuHU6dO6ZNPPilzfEX2D05OTvrrX/9qtPfv31/me5qPj48GDBhgtCu6f1i7dq327t1b6vg/7h8+//xznTx5stTxf9w/LFy4sMz9wwMPPKC7775bUvn2D4888ogaNWokqXz7h0GDBhkffg8fPqwVK1aUOf6P+4dvv/221LF/3D9s3rxZW7duLXW8n5+fHn30UaP93XffKTMzs9TxLVq00IMPPmi0v/jiC2VlZZU6/o/7h08++aTMwH3p/qGkpKTS9w9PPfWUateuLUnKysrS0qVLyxw/fPhwIwT9/PPPWrNmTalj3dzcbM6S3bZtmzZt2lTq+Pr16+vxxx832snJydq1a1ep4++66y716tXLaH/11Vc6cuRIqeODg4PVqVMno/3pp5/qzJkzpY7v0qWLgoKCjPa8efPKDP8PP/ywmjVrJulCuL3032rUqFGSpP/+97/GtEv3D8ePH9eSJUtKnbd04Szki19G/PLLLzbz+iNXV1cNGzbMaO/YsUM//PBDqeN9fX315JNPGu0NGzYoPT291PF33HGHevfubbS//vprHThwoNTxLVu21F/+8hejvXTpUp0+fbrU8eHh4QoNDTXaCxYsKPNLtZ49eyogIEDShfB8tb+T2NhYNWzYUJL066+/6uOPPy5zfHx8vPEl34EDB/TFF1+UOtbJyUkjRoww2jt37lRycnKp48tzIPqmuGYeAAAAAIA/EwdrWV8j3WS2bdum4OBg/fe//zW+6fyj6nwapb1PUbrZTqPkNPvScZo9p1FK1e89itPsq89plH/2/cPp06cVGBgoSfr3v/+t7t27G6//o+q+f+A0+/+P/QP7h4sq6z3q6NGjioyMlHThLMyLZzVymj2n2Usy/j+UhTB/iYthfv369TanVgIAAJTHgQMH1LdvX5vLc+6991598sknatq0qf0KA1DtHDp0SPfee6+kC6dcN27c2M4VoTopzzXznGYPAABQCUpKStSvXz8jyF+8Nnznzp3q169fmUebAACoKMI8AABAJdiwYYNxY9k1a9bo8OHDxk3Adu7cWeZNagEAqCjCPAAAQCXYv3+/pAtH5C/eQb5bt27GdY9lPZUEAICKIswDAABUgttvv12SdOTIEeOxjWvXrjUemXjx0YUAAFQGwjwAAEAlaN++vXEzq8jISDVq1Mi4U/W9996rdu3a2bM8ANXMpffR2Lx5M/fVQIUR5gEAACqBo6OjPvnkE/n7+0uScUT+4t3sS3s8HYCbz8UnX1z0t7/9TR06dNCBAwfsVxRMh70KAABAJWnatKmWLl1qtOfPn68ffviBx9IBMPDkC1QWwjwAAEAluvQIfGhoKEfkAdjgyReoLOxdAAAAAOAG4ckXqCyEeQAAAAC4QXjyBSoLYR4AAAAAbhCefIHK4mzvAqqjkpISFRcX27sMAABgQpd+higuLuYzBYDLfPTRR3rkkUf0888/G0fk77nnHn300UeyWq28b6BcCPNXUFRUpKKiInuXAQAATOiPYZ7PFAD+yM/PT5988olCQkIkSf/+978VFRUlR0dH3jNQboT5K/Dy8pKPj4+9ywAAACaUk5Nj/F6rVi0+UwC4okvfKyIiIlSnTh07VgMzIsxfgZOTk5ycnOxdBgAAMKFLP0PwmQJAaXivwPXiBngAAAAAAJgMYR4AAAAAAJMhzAMAAAAAYDJcMw8AAFCJmjRpIqvVau8yAAB/chyZBwAAAADAZAjzAAAAAACYDGEeAAAAAACTIcwDAAAAAGAyhHkAAAAAAEzG1GE+KSlJjz/+uEJDQ9W2bVsNHz5c2dnZNmO++eYbRUZGqmXLloqNjdXevXvtVC0AAAAAXHDxyRdWq1VNmjSxdzkwIVOH+dzcXA0ePFhJSUlat26d3N3dNXLkSKN/3759GjNmjJ577jlt3rxZbdq0UWJiooqKiuxXNAAAAAAA18nUYb5Xr17q3Lmz3N3d5ebmpv79+2vHjh1GWF+5cqXCwsLUpUsX1ahRQ4mJiTp9+rS2bt1q58oBAAAAALh2pg7zf7RlyxY1a9ZMzs7OkqTMzEw1b97c6HdxcVGzZs2UmZlprxIBAAAAALhuzvYuoDTx8fFKSkoqtX/dunVq1KiR0d61a5dmzJihGTNmGNPy8/Pl5eVl8zovLy/l5eVVer0AAAAAANwo1TbMT506VRaLpdR+b29v4/fMzEwNHjxY48aNU/v27Y3pbm5uys3NtXldbm6u3N3dK71eAAAAAABulGob5j08PMo1LjMzUwMGDNDo0aP18MMP2/QFBAQoIyPDaBcWFmrv3r3y9/ev1FoBAAAAALiRTH3N/J49ezRgwACNHDlSffr0uaw/KipKmzZtUnJysiwWi+bMmSMfHx+FhobaoVoAAAAAACqHqcP8+++/r9OnT+v1119XUFCQ8XPs2DFJ0h133KG33npLkyZNUkhIiDZs2KBZs2YZN8gDAAAAAMCMHKxWq9XeRVQX27ZtU3BwsNLS0tS6dWt7lwMAAAAAwBWZ+sg8AAAAAAA3I8I8AAAAAAAmQ5gHAAAAAMBkCPMAAAAAAJgMYR4AAAAAAJMhzAMAAAAAYDKEeQAAAAAATIYwDwAAAACAyRDmAQAAAAAwGcI8AAAAAAAmQ5gHAAAAAMBkCPMAAAAAAJgMYR4AAAAAAJMhzAMAAAAAYDKEeQAAAAAATIYwDwAAAACAyRDmAQAAAAAwGcI8AAAAAAAmQ5gHAAAAAMBkCPMAAAAAAJgMYR4AAAAAAJMhzAMAAAAAYDKEeQAAAAAATIYwDwAAAACAyRDmAQAAAAAwGcI8AAAAAAAmQ5gHAAAAAMBkCPMAAAAAAJjMnybML1myRAEBAVqwYIHN9G+++UaRkZFq2bKlYmNjtXfvXvsUCAAAAABAJflThPnjx49r3rx58vf3t5m+b98+jRkzRs8995w2b96sNm3aKDExUUVFRXaqFAAAAACA6/enCPMTJ05UYmKivL29baavXLlSYWFh6tKli2rUqKHExESdPn1aW7dutU+hAAAAAABUAtOH+dWrV+vs2bPq3bv3ZX2ZmZlq3ry50XZxcVGzZs2UmZl5AysEAAAAAKByOdu7gNLEx8crKSmp1P5169bJ09NTb775pt5///0rjsnPz5eXl5fNNC8vL+Xl5VVmqQAAAAAA3FDVNsxPnTpVFoul1H5vb2+NGzdOffv2VdOmTa84xs3NTbm5uTbTcnNz5e7uXpmlAgAAAABwQ1XbMO/h4XHVMSkpKTp79qw++OADSdLZs2e1c+dOpaWlaebMmQoICFBGRoYxvrCwUHv37r3sRnkAAAAAAJhJtQ3z5bFkyRIVFxcb7REjRig8PFz9+vWTJEVFRWnBggVKTk5W27Zt9d5778nHx0ehoaH2KhkAAAAAgOtm6jBft25dm7arq6s8PDxUu3ZtSdIdd9yht956S5MmTVJ2drbuvvtuzZo1S87Opl5tAAAAAMBNzsFqtVrtXUR1sW3bNgUHBystLU2tW7e2dzkAAAAAAFyR6R9NBwAAAADAzYYwDwAAAACAyRDmAQAAAAAwGcI8AAAAAAAmQ5gHAAAAAMBkCPMAAAAAAJgMYR4AAAAAAJMhzAMAAAAAYDKEeQAAAAAATIYwDwAAAACAyRDmAQAAAAAwGcI8AAAAAAAmQ5gHAAAAAMBkCPMAAAAAAJgMYR4AAAAAAJMhzAMAAAAAYDKEeQAAAAAATIYwDwAAAACAyRDmAQAAAAAwGcI8AAAAAAAmQ5gHAAAAAMBkCPMAAAAAAJgMYR4AAAAAAJMhzAMAAAAAYDKEeQAAAAAATIYwDwAAAACAyRDmAQAAAAAwGcI8AAAAAAAmY/own5OToxdeeEFhYWFq3bq1oqOjde7cOaP/m2++UWRkpFq2bKnY2Fjt3bvXjtUCAAAAAHD9TB3mS0pKFB8fL2dnZ3399dfaunWrXn31VTk7O0uS9u3bpzFjxui5557T5s2b1aZNGyUmJqqoqMjOlQMAAAAAcO1MHebXr1+vrKwsjRs3Tt7e3nJ0dNTdd98tFxcXSdLKlSsVFhamLl26qEaNGkpMTNTp06e1detWO1cOAAAAAMC1M3WY37x5sxo3bqyxY8cqLCxMPXv21Oeff270Z2Zmqnnz5kbbxcVFzZo1U2Zmpj3KBQAAAACgUjjbu4DSxMfHKykpqdT+devW6cyZM0pNTdW4ceP0xhtv6Mcff9SgQYPUqFEjhYaGKj8/X15eXjav8/LyUl5eXhVXDwAAAABA1am2YX7q1KmyWCyl9nt7e8vNzU0NGjTQE088IUkKDg5W165d9d133yk0NFRubm7Kzc21eV1ubq7c3d2rtHYAAAAAAKpStQ3zHh4eVx3TvHlzrVmzptT+gIAAZWRkGO3CwkLt3btX/v7+lVIjAAAAAAD2YOpr5rt166aCggItWrRIxcXF2rFjh9atW6eIiAhJUlRUlDZt2qTk5GRZLBbNmTNHPj4+Cg0NtXPlAAAAAABcu2p7ZL48vLy89H//93+aMGGCJk+erPr16+ull15SSEiIJOmOO+7QW2+9pUmTJik7O1t33323Zs2aZTy6DgAAAAAAM3KwWq1WexdRXWzbtk3BwcFKS0tT69at7V0OAAAAAABXZOrT7AEAAAAAuBkR5gEAAAAAMBnCPAAAAAAAJkOYBwAAAADAZAjzAAAAAACYDGEeAAAAAACTIcwDAAAAAGAyhHkAAAAAAEyGMA8AAAAAgMkQ5gEAAAAAMBnCPAAAAAAAJkOYBwAAAADAZAjzAAAAAACYjPO1vvDAgQP6/fff5e3traZNm1ZiSQAAAAAAoCwVDvPr16/X+PHjlZWVJavVKgcHBzVs2FAvv/yyOnXqVBU1AgAAAACAS1QozO/Zs0eJiYnq0aOHoqKi1LBhQ2VlZWnlypUaOnSoli1bpoCAgKqqFQAAAAAAqIJh/oMPPtATTzyhf/7zn8a0Zs2aqUOHDqpTp44WLlyoSZMmVXqRAAAAAADg/6vQDfDS0tLUr1+/K/b169dPaWlplVIUAAAAAAAoXYXC/KlTp9S4ceMr9t122206depUpRQFAAAAAABKV6EwX1JSUma/1Wq9rmIAAAAAAMDVVeia+eLiYm3atKnU0F5cXFwpRQEAAAAAgNJVKMyfP39eTz31VKn9Dg4O11sPAAAAAAC4igqF+YyMjKqqAwAAAAAAlFOFrpmXpLy8PO3atUv5+flVUQ8AAAAAALiKCj+arlOnToqOjlaXLl20Y8eOqqoLAAAAAACUokJhfvr06Xr44Yf1xRdfqEePHnrnnXeqqi4AAAAAAFCKCl0zv2fPHs2dO1c1atTQmDFj9MADD1RVXQAAAAAAoBQVOjJvsVhUo0YNSZK7u7ssFkuVFAUAAAAAAEpXoSPzJSUlNs+ZLywsvOy5823btq3cCgEAAAAAgI3rfs78pW0HBwft3r27Muoqt88++0xz587ViRMnVL9+fQ0dOlS9evUy+tPS0jRhwgQdPHhQTZs21fjx4xUUFHRDawQAAAAAoDKZ+jnzu3bt0oQJEzR37lyFhYVp06ZNGjJkiFq0aKE777xTv//+uxISEvT3v/9dvXv31ooVK5SQkKC1a9fKy8vL3uUDAAAAAHBNKvyc+dIUFRVp7NixlTW7cjly5Ij8/PzUpk0bOTg4qG3btmrYsKF++eUXSdLatWtVv359PfLII3J1ddUjjzwiX19frV279obWCQAAAABAZaq0MF9SUqIvvviismZXLh06dJC7u7s2bNigkpISff/998rJyVFwcLAkKTMzU82bN7d5TfPmzZWZmXlD6wQAAAAAoDJV6DT7Gyk+Pl5JSUml9q9bt05+fn6KiorS008/raKiIjk6Ouq1115T3bp1JUn5+fmXnU7v5eWlvLy8qiwdAAAAAIAqVW3D/NSpU8t89J23t7eWLl2qefPm6dNPP5W/v79+/vlnxcfHy8vLS507d5abm5vOnDlj87rc3FzVrl27qssHAAAAAKDKVNpp9pXNw8NDtWvXLvXH0dFRu3fvVseOHdW8eXM5OjqqefPmat++vdavXy9JCggIuOymfRkZGfL397fHKgEAAAAAUCkqdGS+U6dOcnBwqKpaKqxVq1aaOnWq9uzZo7vuukt79uzRDz/8oGeeeUaS1K1bN7355pv67LPP9PDDD+s///mPTp48qW7dutm5cgAAAAAArl2FwvzIkSOrqIxrExUVpaysLCUkJOj06dPy9vZWnz591LdvX0kXTsWfPXu2JkyYoFdeeUVNmzbV7NmzVatWLTtXDgAAAADAtatQmL/lllv04IMPGu1Dhw6pcePGRnvevHmVV1k5xcfHKz4+vtT+kJCQG36XfQAAAAAAqlKFrpl/4YUXbNoXj4Bf9K9//ev6KwIAAAAAAGWqUJi3Wq0VagMAAAAAgMpXoTD/x5vfXa0NAAAAAAAqX7V9NB0AAAAAALiyCt0Az2KxaMaMGUb7/PnzNu3CwsLKqwwAAAAAAFxRhcJ8UFCQtm7darRbtmxp027VqlWlFQYAAAAAAK6sQmH+ww8/rKo6AAAAAABAOXHNPAAAAAAAJkOYBwAAAADAZAjzAAAAAACYDGEeAAAAAACTIcwDAAAAAGAyhHkAAAAAAEyGMA8AAAAAgMkQ5gEAAAAAMBnCPAAAAAAAJkOYBwAAAADAZAjzAAAAAACYDGEeAAAAAACTIcwDAAAAAGAyhHkAAAAAAEyGMA8AAAAAgMkQ5gEAAAAAMBnCPAAAAAAAJkOYBwAAAADAZAjzAAAAAACYDGEeAAAAAACTIcwDAAAAAGAy1TrMnzhxQgkJCerQoYMCAgK0e/fuy8Z88803ioyMVMuWLRUbG6u9e/dWqB8AAAAAALOp1mHe0dFR4eHhmjVr1hX79+3bpzFjxui5557T5s2b1aZNGyUmJqqoqKhc/QAAAAAAmFG1DvO+vr56/PHHFRgYeMX+lStXKiwsTF26dFGNGjWUmJio06dPa+vWreXqBwAAAADAjKp1mL+azMxMNW/e3Gi7uLioWbNmyszMLFc/AAAAAABm5GyvBcfHxyspKanU/nXr1qlRo0ZlziM/P19eXl4207y8vJSXl1eufgAAAAAAzMhuYX7q1KmyWCyl9nt7e191Hm5ubsrNzbWZlpubK3d393L1AwAAAABgRnYL8x4eHtc9j4CAAGVkZBjtwsJC7d27V/7+/uXqBwAAAADAjKr9NfMFBQUqKCiQdCGMFxQUqKSkRJIUFRWlTZs2KTk5WRaLRXPmzJGPj49CQ0PL1Q8AAAAAgBnZ7ch8eV16J/uYmBhJ0sKFCxUWFqY77rhDb731liZNmqTs7GzdfffdmjVrlpydL6zW1foBAAAAADAjB6vVarV3EdXFtm3bFBwcrLS0NLVu3dre5QAAAAAAcEXV/jR7AAAAAABgizAPAAAAAIDJEOYBAAAAADAZwjwAAAAAACZDmAcAAAAAwGQI8wAAAAAAmAxhHgAAAAAAkyHMAwAAAABgMoR5AAAAAABMhjAPAAAAAIDJEOYBAAAAADAZwjwAAAAAACZDmAcAAAAAwGQI8wAAAAAAmAxhHgAAAAAAkyHMAwAAAABgMoR5AAAAAABMhjAPAAAAAIDJEOYBAAAAADAZwjwAAAAAACZDmAcAAAAAwGQI8wAAAAAAmAxhHgAAAAAAkyHMAwAAAABgMoR5AAAAAABMhjAPAAAAAIDJEOYBAAAAADAZwjwAAAAAACZTrcP8iRMnlJCQoA4dOiggIEC7d++26f/888/Vt29fBQcHq0OHDnr++eeVk5NjM2bx4sXq3LmzWrVqpSFDhujEiRM3chUAAAAAAKh01TrMOzo6Kjw8XLNmzbpi/7lz5/T3v/9dGzdu1JdffqmTJ09qwoQJRn9KSoqmTJmiGTNmaOPGjapTp47GjBlzo8oHAAAAAKBKONu7gLL4+vrq8ccfL7W/X79+xu81atTQY489pldeecWYtnz5ckVFRally5aSpNGjRys8PFyHDx/WbbfdVnWFAwAAAABQhar1kfmK2rJliwICAox2ZmamWrRoYbR9fX3l6+urzMxMe5QHAAAAAEClsNuR+fj4eCUlJZXav27dOjVq1Kjc80tOTtZnn32mTz75xJiWn58vT09Pm3FeXl7Ky8urcL0AAAAAAFQXdgvzU6dOlcViKbXf29u73PNKSUnR2LFj9e6779ocmXdzc1Nubq7N2NzcXLm7u1e4XgAAAAAAqgu7hXkPD49KmU9KSopGjBihqVOnqm3btjZ9AQEBysjIMNq//vqrTp48aRP4AQAAAAAwm2p/zXxBQYEKCgokSYWFhSooKFBJSYkkKTU1VcOHD9ebb76p8PDwy14bHR2tlStXKj09XefOndO0adMUGhrKze8AAAAAAKbmYLVarfYuoixXOoq+cOFChYWFKS4uTlu3btUtt9xi0799+3bj90WLFmn27NnKyclRaGioJk2apHr16l1xWdu2bVNwcLDS0tLUunXryl0RAAAAAAAqSbUP8zcSYR4AAAAAYAbV/jR7AAAAAABgizAPAAAAAIDJEOYBAAAAADAZwjwAAAAAACZDmAcAAAAAwGQI8wAAAAAAmAxhHgAAAAAAkyHMAwAAAABgMoR5AAAAAABMhjAPAAAAAIDJEOYBAAAAADAZwjwAAAAAACZDmAcAAAAAwGQI8wAAAAAAmAxhHgAAAAAAkyHMAwAAAABgMoR5AAAAAABMhjAPAAAAAIDJEOYBAAAAADAZwjwAAAAAACZDmAcAAAAAwGQI8wAAAAAAmAxhHgAAAAAAkyHMAwAAAABgMoR5AAAAAABMhjAPAAAAAIDJEOYBAAAAADAZwjwAAAAAACZTrcP8iRMnlJCQoA4dOiggIEC7d+8udey0adMUEBCgb775xmb64sWL1blzZ7Vq1UpDhgzRiRMnqrpsAAAAAACqVLUO846OjgoPD9esWbPKHJeRkaHvvvtOdevWtZmekpKiKVOmaMaMGdq4caPq1KmjMWPGVGXJAAAAAABUuWod5n19ffX4448rMDCw1DHFxcV64YUXNG7cOLm6utr0LV++XFFRUWrZsqXc3Nw0evRobdmyRYcPH67q0gEAAAAAqDLVOsyXx4IFCxQQEKD777//sr7MzEy1aNHCaPv6+srX11eZmZk3skQAAAAAACqVs70WHB8fr6SkpFL7161bp0aNGpU5j8OHD+vjjz/W8uXLr9ifn58vT09Pm2leXl7Ky8urcL0AAAAAAFQXdgvzU6dOlcViKbXf29v7qvMYN26cRowYUepYNzc35ebm2kzLzc2Vu7t7RUoFAAAAAKBasVuY9/DwuO55pKSkaPfu3XrttdckSTk5ORo7dqz69u2r559/XgEBAcrIyDDG//rrrzp58qQCAgKue9kAAAAAANiL3cJ8eRUUFBi/FxYWqqCgQC4uLnJ0dFRycrLN2EcffVTDhg1TZGSkJCk6OlrDhw/Xww8/rLvuukvTpk1TaGiobrvtthu6DgAAAAAAVKZqfwO8wMBA4272MTExCgwM1JYtWyRJDRo0sPlxcnKSj4+PatWqJUlq27atnn32WQ0bNkxt27bViRMnNGXKFLutCwAAAHCzOnLkiPr166fWrVvrscces3c5phQbG6uZM2eWe3xcXJzefvvtKqwI9lTtj8xX5M7z33777WXTYmNjFRsbW5klAbiJHTx4UE2bNpUkHThwQE2aNLFvQQAA3ABxcXHavn27XFxc5ODgoIYNGyouLq5Cofz//u//5OXlpa1bt8rRsdofUwSqPf6KAAAAAFzVwIEDtX37dm3dulVDhw7V+PHjtXnz5nK//tChQwoICLiuIF9YWHjNrwX+bAjzAAAAQDXw+++/l/lzaZAtKSm56vji4mJjfFFRkU3f9XB0dFSPHj1Uq1Yt/fjjj8b0HTt2KC4uTmFhYerSpYumT5+uoqIiSVLXrl2VmpqqefPmKSgoSHPmzJEk7d27V/Hx8WrXrp3Cw8M1fvx45efnG/OMiIjQO++8o4EDByooKEjz58+XJK1YsUJRUVEKDg5Wz5499dVXXxmvSU1NVUBAgFavXq3u3bsrKChIAwYM0PHjx40x58+f19tvv230/+Uvf9Hnn39u9CcnJysmJkahoaGKjIzUwoULy9wmERERmjlzplFnZGSkUlJSlJqaql69eikoKEhPPfWUTp48abwmJydH48aNU8eOHRUWFqZBgwZp3759Rn9RUZHefPNNtW/fXm3atNHUqVMvW+7x48c1evRohYeHG5cYnz59uux/QPxpVPvT7AEAAICbwYwZM8rsj42Nlb+/v6QLQfBq459++mnVq1dPknT06FEtWLDA6Hv55Zevuc6ioiL997//1e+//65mzZpJkvbt26ennnpKr776qrp3767jx4/r6aefVo0aNfT000/rm2++UVxcnFq3bq1Ro0ZJkk6fPq3HH39cCQkJmjlzps6ePatnn31Wr732ml599VVjeUuWLNG7776rVq1a6fz581q+fLneeecdzZw5U/fcc4+2bdumIUOGqH79+goJCTFet3btWi1dulSSNHjwYE2bNk2TJ0+WJL344os6cOCAZs2apTvuuEMnTpzQqVOnJEmbNm3S6NGjNXPmTIWFhemXX37R4MGD5e3traioqFK3y/LlyzV79mzdddddeuutt/T3v/9drVq10gcffCBXV1cNGjRIM2fO1MSJEyVJY8eOVV5enpYtWyYPDw9NmzZNAwYM0KpVq+Tu7q65c+dq9erV+uCDD9S4cWPNmjVL6enpateunSTJYrGof//+6tKli77++mtZrVaNHz9eo0ePNr70wJ8bR+YBAAAAXNX777+vkJAQBQYG6h//+IfGjBmjzp07S5I++eQTdenSRT179pSzs7P8/Pw0aNAgLV++vNT5/ec//1GTJk301FNPydXVVbVr19YzzzyjFStW2JxVEB0draCgIDk4OKhmzZqaP3++EhISdN9998nR0VEhISHq0aOHzZF1SRo9erQ8PT3l6empXr16KT09XdKFLxG++OILvfzyy2rWrJkcHBxUv3593XPPPZKkBQsWKDY2Vm3btpWjo6P8/f312GOPlbkuktS3b181b95cTk5O6t27t06ePKmBAweqdu3a8vDwUGRkpFHDiRMn9N133+mFF15Q3bp1VbNmTY0dO1bnz59XUlKSpAtfDgwYMEB33nmnXF1d9cwzz8jLy8tYXlJSkvLy8jR27Fi5ubnJ3d1dY8aM0caNG5WdnV2+f1SYGkfmAQAAgGpgxIgRZfa7u7sbv3t5eV11vKenp/G7n5/fVcdfzd/+9jeNGjVKZ8+e1euvv64NGzZowIABcnZ21oEDB5SammpzZLykpERWq7XU+R04cEA//fSTzWusVqscHBx06tQp1a9fX5LUqFEjm9cdPHhQkydPtnlKVXFxsc18JBlnJUhSzZo1lZeXJ+nCXfUl6fbbb79iXQcPHlRKSooWLVpkM/9bb7211HW50vIkqW7dules4WLYvvSR2S4uLvLz89OxY8eMMZeuu5OTkxo2bGi0Dxw4oF9//VWhoaE2dbi6uurYsWNq0KBBmfXC/AjzAAAAQDXg7e1d7rGOjo4VGu/s7Fyh8WXx8PDQyy+/rAcffFAff/yx+vfvr7p16+qhhx7S66+/Xu751K1bV61bt77q9eh/vGGer6+vhg8frt69e19L+UZAPnDggO69997L+n19fdWzZ08NGzbsmuZfHheD9uHDh9W8eXNJFy5fOHbsmPGlQYMGDXT06FHjNcXFxTZH3OvWratbb71V33zzTZXVieqN0+wBAAAAVIirq6uGDh2qWbNmKTc3V7Gxsfr666/13//+VxaLRcXFxTp48KDWr19f6jyio6O1e/duffzxxzp37pysVquysrKuGk779+9vXD9eUlIii8Wi9PR07dy5s1y1165dWw899JAmTpxo3HDuxIkT+umnn4z5f/TRR0pJSVFRUZGKior0888/a8uWLeXcOldXr149derUSW+88YZOnTql8+fPa8qUKXJxcVGnTp0kSb1799b777+vvXv3ymKx6F//+pfOnDljzKNbt24qKirS9OnTlZubK0n69ddftWrVqkqrE9UbYR4AAABAhT388MPy9vbWv//9bwUGBmr+/Pn67LPPjLuzDx8+3Dhl/EpuvfVWLV68WBs3blS3bt0UEhKigQMHKjMzs8zl9u/fX8OGDdP48eN1//33Kzw8XG+99ZbOnTtX7tpfeeUVhYaGavDgwQoKClJsbKz27Nkj6cKd9998803NmDFD7dq1U7t27fTiiy/qt99+K/f8y+PNN9+Un5+f/vrXv6pTp07as2ePFixYIA8PD0kXbtoXGRmpuLg4dezYUYWFhQoMDDRe7+HhoSVLligrK0u9evVS69at9dhjj1Xqlw6o3hysZV3IcpPZtm2bgoODlZaWptatW9u7HADV0P79+3XHHXdIkhYtWqRHHnnkup6XCwAAAFwLPoECQDnt379fPXr0MNqxsbEKCgrS/v377VgVAAAAbkaEeQAoh5KSEvXu3VsZGRmS/v/Nc9LT09W7d2+VlJTYszwAAADcZAjzAFAO69evN54Nu2bNGh0+fFhr1qyRdCHQf//99/YsDwAAADcZwjwAlMPevXslXTgi361bN0kX7iLr5+cnSfrll1/sVhsAAABuPoR5ACiHZs2aSZKOHDmitWvXSpLWrl1rPP/1zjvvtFttAAAAuPkQ5gGgHDp27Gg8DiYyMlKNGjVSZGSkJCkwMFDh4eH2LA8AAAA3GcI8AJSDo6OjVqxYoebNm0uScUQ+MDBQK1as4PF0AAAAuKH49AkA5XT77bdr1apVRnvx4sXavn27br/9djtWBQC4WR08eFAODg5ycHDQwYMH7V0OgBuMMA8AFXDpEfg2bdpwRB4AAAB2wadQAAAAwIRKSkqM31NSUmza1VFcXJzefvtte5ehgwcPKiAgQEeOHCnX+CNHjiggIICzH1DtEOYBAAAAk9m/f7969OhhtGNjYxUUFKT9+/dX2TIPHz6sZ599Vh06dFBQUJA6dOigwYMH68SJE1W2TAClI8wDAAAAJlJSUqLevXsrIyNDktSoUSNJUnp6unr37l1lR+iHDBkid3d3ffnll9q+fbtWrFihHj16yMHBoUqWB6BshHkAAADARNavX6/09HRJ0po1a3T48GGtWbNG0oVA//3331f6Mn/77Tft27dPjz32mLy9vSVJvr6++utf/6q6detq+fLl6tixo81rZs6cqdjYWJtpubm5GjZsmIKCgtStWzetWLHC6EtNTVVAQIBWrVql7t27q2XLlkpISFBOTo6mT5+uDh06KCwsTO+8847NPLdv365+/fopNDRUERERmjJliiwWi9F/6NAh9e/fX61bt9aDDz6o1NTUy9YvOTlZMTExCg0NVWRkpBYuXFjqtsjIyNATTzyhkJAQhYaGKjo6Wvv27SvvpgQqDWEeAAAAMJG9e/dKunBEvlu3bpKkbt26yc/PT5L0yy+/VPoyfXx85O/vr5deeknLli1TRkbGNZ0BsHTpUkVHR2vLli168cUX9eKLLyotLc1mTFJSkpYtW6Zvv/1W+/fvV0xMjHx9fZWUlKT33ntPc+bM0fbt2yVJx44d04ABAxQZGakNGzbo/fff13fffae33npLklRcXKyEhATdeuut+uGHHzR//nx9+umnNsvbtGmTRo8erWeffVapqal69913NW/ePK1cufKK6zB+/Hi1adNGmzZtUkpKiiZNmiQvL68KbwvgehHmAQAAABNp1qyZpAs3Zlu7dq0kae3atTp69Kgk6c4776yS5S5cuFAdO3bUokWLFBMTozZt2mjy5Mk2R8GvplOnToqIiJCzs7M6deqkrl27atmyZTZjRo0aJQ8PD9WpU0edOnWSJD3xxBNydnZWq1at1KxZM+PMhC+++EJNmzbVU089JVdXVzVt2lQjR47Up59+KqvVqv/973/av3+/nn/+ebm5ualBgwZKTEy0Wd6CBQsUGxurtm3bytHRUf7+/nrssce0fPnyK66Di4uLsrKydOzYMTk7O6tFixby9fWtyKYEKoWzvQsAAAAAUH4dO3ZUYGCg0tPTFRkZKT8/PyPIBwYGKjw8vEqW6+PjoxEjRmjEiBGyWCxav369/vGPf8jNzc04K+BqLl7ff2l7165dNtPq1q1r/F6zZk3Vq1fPpr9mzZrKy8uTJGVlZalx48Y2/Y0bN9b58+d1+vRpHT9+XF5eXvL09Cy1hoMHDyolJUWLFi0yphUXF+vWW2+94jq88cYbmj17tvr376/i4mJ1795dI0eOlLu7+9VWH6hUhHkAAADARBwdHY2bz2VkZNgE+RUrVsjRsepPvnV1dVXXrl3Vrl07/fTTT/L399e5c+dsxlzpLvcXa720Xb9+/Wuuo2HDhtqxY4fNtMOHD+uWW25R7dq1Vb9+feXk5Cg3N9cI9H+swdfXVz179tSwYcPKtUw/Pz+9+uqrki58EZCYmCg3NzeNGjXqmtcDuBacZg8AFdCkSRNZrVZZrVY1adLE3uUAAG5St99+u1atWmW0Fy9erO3bt+v222+vkuWdOXNGU6ZMUUZGhiwWi4qLi5WSkqLU1FSFhobq7rvvVl5enlatWqWSkhKlpqZq9erVl80nOTlZSUlJKi4u1vr167V27Vr16dPnmut66KGHtH//fn344YeyWCw6dOiQZsyYoZiYGDk4OKhly5Zq0qSJXn/9deXn5+v48eOaPXu2zTz69++vjz76SCkpKSoqKlJRUZF+/vlnbdmy5YrLXL58ubKzs2W1WuXh4SEnJyc5OTld8zoA14oj8wAAAIAJXXoEvk2bNlV6RN7FxUW//fabRowYoRMnTsjJyUkNGjTQoEGD9Le//U0ODg4aN26cJk+erBdffFGdO3dWdHS0cW37RX369NHSpUs1atQo1alTRxMmTFBISMg11+Xn56d58+ZpypQpmjFjhjw9PfXggw9qxIgRkiRnZ2fNmTNHL730kjp06KD69etrwIABNkfzu3btKldXV82YMcO4K33Tpk01aNCgKy4zNTVV06ZN09mzZ+Xh4aGIiAgNHjz4mtcBuFYOVqvVau8iqott27YpODhYaWlpat26tb3LAQAAAEp18OBBNW3aVJJ04MABzhgDbjLV+jT7EydOKCEhQR06dFBAQIB279592Zjs7GwNHz5cISEhCgkJ0cCBA236Fy9erM6dO6tVq1YaMmTIFa/dAQAAAMyGS7+Am1u1DvOOjo4KDw/XrFmzrtifn5+vJ598Us2bN1dSUpI2bdpknFIjSSkpKcYpNxs3blSdOnU0ZsyYG1U+AAAAAABVolpfM+/r66vHH3+81P7PP/9cPj4+Ns+KDAwMNH5fvny5oqKi1LJlS0nS6NGjFR4ersOHD+u2226rusIBAAAAAKhC1frI/NVs3rxZ9evX16BBg3T//fcrOjpaycnJRn9mZqZatGhhtH19feXr66vMzEx7lAsAAAAAQKWw25H5+Ph4JSUlldq/bt06NWrUqMx5nDlzRqmpqZo5c6Zmz56t5ORkDR8+XCtXrlSTJk2Un59vPE/yIi8vL+Xl5VXGKgAAAAAAYBd2C/NTp06VxWIptd/b2/uq83Bzc1NQUJC6du0q6cJjJe655x798MMPatKkidzc3JSbm2vzmtzcXLm7u19X7QAAAAAA2JPdwryHh8d1z6N58+batGlTqf0BAQHKyMgw2r/++qtOnjypgICA6142AAAAAAD2Uu2vmS8oKFBBQYEkqbCwUAUFBSopKZEk9e7dW7t27dJ3332nkpISfffdd9q1a5fCw8MlSdHR0Vq5cqXS09N17tw5TZs2TaGhodz8DgAAAABgag5Wq9Vq7yLKcqWj6AsXLlRYWJgkKTk5WZMnT1ZWVpYaN26s0aNHq2PHjsbYRYsWafbs2crJyVFoaKgmTZqkevXqXXFZ27ZtU3BwsNLS0tS6deuqWSEAAAAAAK5TtQ/zNxJhHgAAAABgBtX+NHsAAAAAAGCLMA8AAAAAgMnY7W721dG5c+ckSbt377ZzJQAAAACAm1nz5s3l5uZWaj9h/hIHDhyQJD3xxBP2LQQAAAAAcFO72r3cuAHeJU6dOqWvv/5aTZs2Vc2aNe1dDgAAAADgJnW1I/OEeQAAAAAATIYb4AEAAAAAYDKEeQAAAAAATIYwDwAAAACAyRDmAaCcCgsLNXHiRIWGhur+++/XK6+8oqKiInuXBaCa+eijjxQdHa17771XiYmJ9i4HQDVksVj04osvKiIiQkFBQXrggQe0dOlSe5cFk+HRdABQTrNnz1ZaWpq++uorSdLgwYM1Z84cDRs2zM6VAahO6tWrp8TERG3cuFHZ2dn2LgdANVRUVKS6detqwYIFuu2227Rjxw4NHjxYDRo0UIcOHexdHkyCI/MAUE7Lli3T008/rXr16qlevXpKSEjQsmXL7F0WgGomMjJSXbt2lY+Pj71LAVBNubm5acSIEWrcuLEcHBzUqlUrhYWFKS0tzd6lwUQI8wBQDmfOnFF2drZatGhhTGvRooWOHTum3NxcO1YGAADMrqCgQOnp6QoICLB3KTARwjwAlEN+fr4kydPT05jm5eUlScrLy7NLTQAAwPysVqteeOEFNWnSRJGRkfYuByZCmAeAcnBzc5MknT171ph28Yi8u7u7XWoCAADmZrVaNX78eO3fv1+zZs2SoyPxDOXH/xYAKIdatWqpQYMG2r17tzFt9+7datiwoc3RegAAgPKwWq2aMGGC0tPT9f777/N5AhVGmAeAcoqOjtacOXN08uRJnTx5Uu+995769u1r77IAVDNFRUUqKChQUVGRSkpKVFBQIIvFYu+yAFQzEydO1LZt2/T++++rVq1a9i4HJuRgtVqt9i4CAMygsLBQr732mr788ktJUlRUlJ577jk5O/OUTwD/38yZM/Xuu+/aTLv//vv14Ycf2qkiANXN0aNHFRERIVdXV5vPEb169dLEiRPtWBnMhDAPAAAAAIDJcJo9AAAAAAAmQ5gHAAAAAMBkCPMAAAAAAJgMYR4AAAAAAJMhzAMAAAAAYDKEeQAAAAAATIYwDwAAAACAyRDmAQAAAAAwGcI8AAB/QnFxcXr77bftXYYOHjyogIAAHTlypFzjjxw5ooCAAB08eLDCy9q6dauCgoJUXFxc4dcCAGA2zvYuAAAA2Dp8+LDefvttbd68WXl5eXJ3d1eLFi00adIk1atXz97lVVshISHavn27vcsAAOCGIMwDAFDNDBkyRCEhIfryyy/l7e2tU6dO6fvvv5eDg4O9SwMAANUEp9kDAFCN/Pbbb9q3b58ee+wxeXt7S5J8fX3117/+VXXr1pUkLV++XB07drR53cyZMxUbG2szLTc3V8OGDVNQUJC6deumFStWGH2pqakKCAjQqlWr1L17d7Vs2VIJCQnKycnR9OnT1aFDB4WFhemdd96xmef27dvVr18/hYaGKiIiQlOmTJHFYjH6Dx06pP79+6t169Z68MEHlZqaetk6JicnKyYmRqGhoYqMjNTChQvLvX1ycnI0atQohYWFqXXr1urevbtWr15ts05FRUWSpPbt2ysoKMj4ueeeexQXF1cpdQAAYG8cmQcAoBrx8fGRv7+/XnrpJfXr10/33HOP/P395ehY8e/fly5dqunTp2v69OnasGGDhg4dqttuu03BwcHGmKSkJC1btkwFBQXq16+fYmJiFBcXp6SkJO3cuVP9+vVTeHi4goKCdOzYMQ0YMEAjR47UggULdOzYMQ0dOlQFBQV64YUXVFxcrISEBLVs2VKzZ89WTk6Ohg0bZlPTpk2bNHr0aM2cOVNhYWH65ZdfNHjwYHl7eysqKuqq6zRv3jzl5eVp3bp1cnd317Fjx3Tu3Lkrjt2wYYPx+88//6y4uDj16dOnUuoAAMDeODIPAEA1s3DhQnXs2FGLFi1STEyM2rRpo8mTJ9scAS+PTp06KSIiQs7OzurUqZO6du2qZcuW2YwZNWqUPDw8VKdOHXXq1EmS9MQTT8jZ2VmtWrVSs2bNlJ6eLkn64osv1LRpUz311FNydXVV06ZNNXLkSH366aeyWq363//+p/379+v555+Xm5ubGjRooMTERJvlLViwQLGxsWrbtq0cHR3l7++vxx57TMuXLy/XOrm4uOj333/Xvn37ZLVa5efnpzvvvLPM1xw9elSDBg3SkCFD1Lt370qpAwAAe+PIPAAA1YyPj49GjBihESNGyGKxaP369frHP/4hNzc3PfPMM+WeT6NGjS5r79q1y2baxVP3JalmzZqX3WCvZs2aysvLkyRlZWWpcePGNv2NGzfW+fPndfr0aR0/flxeXl7y9PQstYaDBw8qJSVFixYtMqYVFxfr1ltvLdc6DRw4UMXFxXrxxReVlZWltm3bavTo0WrSpMkVx58+fVoDBw5Uz549NXDgwEqrAwAAeyPMAwBQjbm6uqpr165q166dfvrpJ0mSu7v7ZaeWnzhx4rLXHj169LJ2/fr1r7mWhg0baseOHTbTDh8+rFtuuUW1a9dW/fr1lZOTo9zcXCPQ/7EGX19f9ezZ87LT78urZs2axhcdv//+uyZOnKh//OMfWrx48WVj8/PzFR8fr8DAQI0dO7ZS6wAAwN44zR4AgGrkzJkzmjJlijIyMmSxWFRcXKyUlBSlpqYqNDRUknT33XcrLy9Pq1atUklJiVJTU42bwF0qOTlZSUlJKi4u1vr167V27VrjmvFr8dBDD2n//v368MMPZbFYdOjQIc2YMUMxMTFycHBQy5Yt1aRJE73++uvKz8/X8ePHNXv2bJt59O/fXx999JFSUlJUVFSkoqIi/fzzz9qyZUu5ali3bp327NmjoqIi1axZUzVq1JCz8+XHJoqKivTMM8/Ix8dHr7322mVPArjeOgAAsDeOzAMAUI24uLjot99+04gRI3TixAk5OTmpQYMGGjRokP72t79Jkm677TaNGzdOkydP1osvvqjOnTsrOjrauLb9oj59+mjp0qUaNWqU6tSpowkTJigkJOSaa/Pz89O8efM0ZcoUzZgxQ56ennrwwQc1YsQISZKzs7PmzJmjl156SR06dFD9+vU1YMAAm6P5Xbt2laurq2bMmKF9+/ZJkpo2bapBgwaVq4YjR45o8uTJOnnypFxdXdWyZUtNmjTpsnHZ2dn64YcfVKNGDeNLEEkKDg7W3Llzr7sOAADszcFqtVrtXQQAAAAAACg/TrMHAAAAAMBkCPMAAAAAAJgMYR4AAAAAAJMhzAMAAAAAYDKEeQAAAAAATIYwDwAAAACAyRDmAQAAAAAwGcI8AAAAAAAmQ5gHAAAAAMBkCPMAAAAAAJgMYR4AAAAAAJP5f4k9Fs06FioIAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -236,7 +228,7 @@
     }
    ],
    "source": [
-    "cmp, ax = ppi.plot_compare(plot=True, figsize=(10, 4));"
+    "cmp, ax = ppi.compare();"
    ]
   },
   {
@@ -256,7 +248,7 @@
     {
      "data": {
       "text/plain": [
-       "y ~ x1"
+       "model_size 2, terms ['Intercept', 'x1']"
       ]
      },
      "execution_count": 8,
@@ -285,7 +277,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 2484x552 with 3 Axes>"
       ]
@@ -314,7 +306,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -324,7 +316,7 @@
     }
    ],
    "source": [
-    "ppi.plot_densities(submodels=[2], kind=\"forest\", figsize=(10, 4));"
+    "ppi.plot_densities(submodels=[1], kind=\"forest\", figsize=(10, 4));"
    ]
   },
   {
@@ -348,7 +340,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.13 ('kulprit')",
+   "display_name": "pymc",
    "language": "python",
    "name": "python3"
   },
@@ -362,12 +354,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "e258224d959b44f4182f17a74d14a0d47bab7e050ff9dffaf26beb16ea41a16f"
-   }
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
diff --git a/kulprit/data/submodel.py b/kulprit/data/submodel.py
index 0be2682..ee1ba8c 100644
--- a/kulprit/data/submodel.py
+++ b/kulprit/data/submodel.py
@@ -3,7 +3,7 @@
 from dataclasses import dataclass
 
 import arviz
-import bambi
+import bambi as bmb
 
 
 @dataclass
@@ -15,19 +15,22 @@ class SubModel:
             extract a built pymc model.
         idata (InferenceData): The inference data object of the submodel containing the
             projected posterior draws and log-likelihood.
-        loss (float): The final loss (negative log-likelihood) of the submodel following
-            projection predictive inference
         size (int): The number of common terms in the model, not including the intercept
         term_names (list): The names of the terms in the model, including the intercept
     """
 
-    model: bambi.models.Model
+    model: bmb.models.Model
     idata: arviz.InferenceData
     loss: float
     size: int
     term_names: list
+    has_intercept: bool
 
     def __repr__(self) -> str:
         """String representation of the submodel."""
+        if self.has_intercept:
+            intercept = ["Intercept"]
+        else:
+            intercept = []
 
-        return ", ".join([self.model.formula.main] + list(self.model.formula.additionals))
+        return f"model_size {self.size}, terms {intercept + self.term_names}"
diff --git a/kulprit/plots/plots.py b/kulprit/plots/plots.py
index 34284ac..67bc9cf 100644
--- a/kulprit/plots/plots.py
+++ b/kulprit/plots/plots.py
@@ -27,7 +27,7 @@ def plot_compare(cmp_df, legend=True, title=True, figsize=None, plot_kwargs=None
         plot_kwargs = {}
 
     if figsize is None:
-        figsize = (len(cmp_df) - 1, 10)
+        figsize = (10, 4)
 
     figsize, ax_labelsize, _, xt_labelsize, linewidth, _ = _scale_fig_size(figsize, None, 1, 1)
 
@@ -105,13 +105,12 @@ def plot_densities(
     var_names=None,
     submodels=None,
     include_reference=True,
-    labels="formula",
+    labels="size",
     kind="density",
     figsize=None,
     plot_kwargs=None,
 ):
     """Compare the projected posterior densities of the submodels"""
-
     if plot_kwargs is None:
         plot_kwargs = {}
     plot_kwargs.setdefault("figsize", figsize)
@@ -119,12 +118,14 @@ def plot_densities(
     if kind not in ["density", "forest"]:
         raise ValueError("kind must be one of 'density' or 'forest'")
 
+    if submodels is None:
+        submodels = path.values()
+    else:
+        submodels = [path[key] for key in submodels]
+
     # set default variable names to the reference model terms
     if not var_names:
-        var_names = list(
-            set(model.components[model.family.likelihood.parent].common_terms.keys())
-            - set([model.response_component.term.name])
-        )
+        var_names = [fvar.name for fvar in model.backend.model.free_RVs]
 
     if include_reference:
         data = [idata]
@@ -134,13 +135,8 @@ def plot_densities(
         data = []
         l_labels = []
 
-    if submodels is None:
-        submodels = path.values()
-    else:
-        submodels = [path[key] for key in submodels]
-
     if labels == "formula":
-        l_labels.extend([submodel.model.formula for submodel in submodels])
+        l_labels.extend([",".join(submodel.term_names) for submodel in submodels])
     else:
         l_labels.extend([submodel.size for submodel in submodels])
 
@@ -167,13 +163,3 @@ def plot_densities(
         )
 
     return axes
-
-
-def align_yaxis(axes, v_1, ax2, v_2):
-    """adjust ax2 ylimit so that v2 in ax2 is aligned to v1 in axes"""
-    _, y_1 = axes.transData.transform((0, v_1))
-    _, y_2 = ax2.transData.transform((0, v_2))
-    inv = ax2.transData.inverted()
-    _, d_y = inv.transform((0, 0)) - inv.transform((0, y_1 - y_2))
-    miny, maxy = ax2.get_ylim()
-    ax2.set_ylim(miny + d_y, maxy + d_y)
diff --git a/kulprit/projection/projector.py b/kulprit/projection/projector.py
index 13a65ca..284859b 100644
--- a/kulprit/projection/projector.py
+++ b/kulprit/projection/projector.py
@@ -1,19 +1,21 @@
+# pylint: disable=too-many-instance-attributes
 """Base projection class."""
 
-import copy
-from typing import Optional, List, Union, Sequence, Dict
 import collections
+from typing import Optional, Sequence, Union
 
 import arviz as az
 import bambi as bmb
-from xarray_einstats.stats import XrContinuousRV, XrDiscreteRV
-
-from scipy import stats
-
 import numpy as np
 
 from kulprit.data.submodel import SubModel
-from kulprit.projection.solver import Solver
+from kulprit.projection.pymc_io import (
+    compile_mllk,
+    compute_llk,
+    compute_new_model,
+    get_model_information,
+)
+from kulprit.projection.solver import solve
 
 
 class Projector:
@@ -21,6 +23,8 @@ def __init__(
         self,
         model: bmb.Model,
         idata: az.InferenceData,
+        has_intercept: bool,
+        num_samples: int,
         path: Optional[dict] = None,
     ) -> None:
         """Reference model builder for projection predictive model selection.
@@ -44,14 +48,22 @@ def __init__(
         # log reference model and reference inference data object
         self.model = model
         self.idata = idata
+        self.has_intercept = has_intercept
+        self.num_samples = num_samples
 
-        # log properties of the reference model
+        # log properties of the reference Bambi model
         self.response_name = model.response_component.term.name
         self.ref_family = self.model.family.name
         self.priors = self.model.constant_components
 
-        # build solver
-        self.solver = Solver(model=self.model, idata=self.idata)
+        self.observed_data = self.get_observed_data()
+        self.pps = self.get_pps()
+        self.base_terms = self.get_base_terms()
+
+        # log properties of the reference PyMC model
+        self.pymc_model = model.backend.model
+        self.all_terms = [fvar.name for fvar in self.pymc_model.free_RVs]
+        self.ref_var_info = get_model_information(self.pymc_model)
 
         # log search path
         self.path = path
@@ -108,10 +120,12 @@ def project(
             raise UserWarning("Please pass either a list, tuple, or integer.")
 
     def project_names(self, term_names: Sequence[str]) -> SubModel:
-        """Primary projection method for GLM reference model.
+        """Primary projection method for reference model.
 
         The projection is defined as the values of the submodel parameters minimizing the
         Kullback-Leibler divergence between the submodel and the reference model.
+        This is achieved by maximizing the log-likelihood of the submodel wrt the predictions of
+        the reference model.
 
         Parameters:
         ----------
@@ -124,150 +138,74 @@ def project_names(self, term_names: Sequence[str]) -> SubModel:
             kulprit.data.ModelData: Projected submodel ``ModelData`` object
         """
 
-        # copy term names to avoid mutating the input
-        term_names_ = copy.copy(term_names)
-
-        # build restricted bambi model
-        new_model = self._build_restricted_model(term_names=term_names_)
-
-        # extract the design matrix from the model
-        d_component = new_model.distributional_components[new_model.family.likelihood.parent]
-
-        if d_component:
-            X = d_component.design.common.design_matrix
-            slices = d_component.design.common.slices
-
-            # Add offset columns to their own design matrix
-            # Remove them from the common design matrix.
-            if hasattr(new_model, "offset_terms"):  # pragma: no cover
-                for term in new_model.offset_terms:
-                    term_slice = slices[term]
-                    X = np.delete(X, term_slice, axis=1)
-
-        # build new term_names (add dispersion parameter if included)
-        term_names_, slices = self._extend_term_names(
-            new_model=new_model,
-            term_names=term_names_,
-            slices=slices,
+        term_names_ = self.base_terms + term_names
+        new_model = compute_new_model(
+            self.pymc_model, self.ref_var_info, self.all_terms, term_names_
         )
 
-        # compute projected posterior
-        projected_posterior, loss = self.solver.solve(term_names=term_names_, X=X, slices=slices)
-
-        # add observed data component of projected idata
-        observed_data = {
-            self.response_name: self.idata.observed_data.get(self.response_name)
-            .to_dict()
-            .get("data")
-        }
-
-        # build idata object for the projected model
-        new_idata = az.InferenceData(
-            posterior=projected_posterior,
-            observed_data=az.convert_to_dataset(observed_data),
+        model_log_likelihood, old_y_value, obs_rvs = compile_mllk(new_model)
+        initial_guess = np.concatenate(
+            [np.ravel(value) for value in new_model.initial_point().values()]
         )
+        var_info = get_model_information(new_model)
 
-        # compute the log-likelihood of the new submodel and add to idata
-        log_likelihood = self.compute_model_log_likelihood(model=new_model, idata=new_idata)
-        new_idata.add_groups(
-            log_likelihood={self.response_name: log_likelihood},
-            dims={self.response_name: [f"{self.response_name}_dim_0"]},
+        new_idata, loss = solve(
+            model_log_likelihood,
+            self.pps,
+            initial_guess,
+            var_info,
         )
+        # restore obs_rvs value in the model
+        new_model.rvs_to_values[obs_rvs] = old_y_value
+
+        # Add observed data to the projected InferenceData object
+        new_idata.add_groups(observed_data=self.observed_data)
+        # Add log-likelihood to the projected InferenceData object
+        new_idata.add_groups(log_likelihood=compute_llk(new_idata, new_model))
 
         # build SubModel object and return
         sub_model = SubModel(
             model=new_model,
             idata=new_idata,
             loss=loss,
-            size=len(new_model.components[new_model.family.likelihood.parent].common_terms),
+            size=len(new_model.free_RVs) - len(self.base_terms),
             term_names=term_names,
+            has_intercept=self.has_intercept,
         )
         return sub_model
 
-    def compute_model_log_likelihood(self, model, idata):
-        # extract observed data
-        obs_array = self.idata.observed_data[model.response_component.term.name]
-        obs_array = obs_array.expand_dims(
-            chain=idata.posterior.dims["chain"],
-            draw=idata.posterior.dims["draw"],
-        )
-
-        preds = model.predict(idata, kind="response_params", inplace=False).posterior[
-            f"{model.family.likelihood.parent}"
-        ]
-        if model.family.name == "gaussian":
-            # initialise probability distribution object
-            dist = XrContinuousRV(
-                stats.norm,
-                preds.values,
-                idata.posterior["sigma"],
-            )
-        elif model.family.name == "binomial":
-            # initialise probability distribution object
-            dist = XrDiscreteRV(
-                stats.binom,
-                n=model.response.data[:, 1],
-                p=preds.values,
-            )
-        elif model.family.name == "bernoulli":
-            # initialise probability distribution object
-            dist = XrDiscreteRV(
-                stats.bernoulli,
-                p=preds.values,
-            )
-        elif model.family.name == "poisson":
-            # initialise probability distribution object
-            dist = XrDiscreteRV(
-                stats.poisson,
-                mu=preds.values,
-            )
-        else:
-            raise NotImplementedError(f"The {model.family.name} family is not yet implemented.")
-
-        # compute log likelihood of model
-        if isinstance(dist, XrContinuousRV):
-            log_likelihood = dist.logpdf(obs_array).transpose(  # pylint: disable=no-member
-                *("chain", "draw", ...)
-            )
-        else:
-            log_likelihood = dist.logpmf(obs_array).transpose(  # pylint: disable=no-member
-                *("chain", "draw", ...)
-            )
-        return log_likelihood
-
-    def _build_restricted_formula(self, term_names: List[str]) -> str:
-        """Build the formula for the restricted model."""
-
-        formula = (
-            f"{self.response_name} ~ " + " + ".join(term_names)
-            if len(term_names) > 0
-            else f"{self.response_name} ~ 1"
-        )
-        return formula
-
-    def _build_restricted_model(self, term_names: List[str]) -> bmb.Model:
-        """Build the restricted model in Bambi."""
-
-        new_formula = self._build_restricted_formula(term_names=term_names)
-        new_model = bmb.Model(new_formula, self.model.data, family=self.ref_family)
-        return new_model
-
-    def _extend_term_names(
-        self,
-        new_model: bmb.Model,
-        term_names: List[str],
-        slices: Dict[str, slice],
-    ) -> List[str]:
+    def get_observed_data(self):
+        """Extract the observed data from the reference model."""
+        observed_data = {
+            self.response_name: self.idata.observed_data.get(self.response_name)
+            .to_dict()
+            .get("data")
+        }
+        return az.convert_to_dataset(observed_data)
+
+    def get_pps(self):
+        """Extract the posterior predictive samples from the reference model."""
+        if "posterior_predictive" not in self.idata.groups():
+            self.model.predict(self.idata, kind="response", inplace=True)
+
+        pps = az.extract(
+            self.idata,
+            group="posterior_predictive",
+            var_names=[self.response_name],
+            num_samples=self.num_samples,
+        ).values.T
+        return pps
+
+    def get_base_terms(self):
         """Extend the model term names to include dispersion terms."""
 
+        base_terms = []
         # add intercept term if present
-        if bmb.formula.formula_has_intercept(new_model.formula.main):
-            term_names.insert(0, "Intercept")
+        if self.has_intercept:
+            base_terms.append("Intercept")
 
         # add the auxiliary parameters
         if self.priors:
             aux_params = [f"{str(k)}" for k in self.priors]
-            term_names += aux_params
-            # TODO generalize  #pylint: disable=fixme
-            slices[aux_params[0]] = slice(-1, None, None)
-        return term_names, slices
+            base_terms += aux_params
+        return base_terms
diff --git a/kulprit/projection/pymc_io.py b/kulprit/projection/pymc_io.py
new file mode 100644
index 0000000..d3de476
--- /dev/null
+++ b/kulprit/projection/pymc_io.py
@@ -0,0 +1,77 @@
+"""Functions to interact with PyMC models"""
+import numpy as np
+
+from pymc import do, compute_log_likelihood
+from pymc.util import is_transformed_name, get_untransformed_name
+from pymc.pytensorf import compile_pymc, join_nonshared_inputs
+from pytensor import function
+from pytensor.tensor import matrix
+
+
+def compile_mllk(model):
+    """
+    Compile the log-likelihood function for the model to be able to condition on both
+    data and parameters.
+    """
+    obs_rvs = model.observed_RVs[0]
+    old_y_value = model.rvs_to_values[obs_rvs]  # do we need to restore the value
+    new_y_value = obs_rvs.type()
+    model.rvs_to_values[obs_rvs] = new_y_value
+
+    vars_ = model.value_vars
+    initial_point = model.initial_point()
+
+    [logp], raveled_inp = join_nonshared_inputs(
+        point=initial_point, outputs=[model.datalogp], inputs=vars_
+    )
+
+    rv_logp_fn = compile_pymc([raveled_inp, new_y_value], logp)
+    rv_logp_fn.trust_input = True
+
+    def fmodel(params, obs):
+        return -rv_logp_fn(params, obs).sum()
+
+    return fmodel, old_y_value, obs_rvs
+
+
+def compute_new_model(model, ref_var_info, all_terms, term_names):
+    """
+    Compute a new model by excluding the terms not in term_names.
+    """
+    exclude_terms = {term: (0,) for term in set(all_terms) - set(term_names)}
+    for term in set(all_terms) - set(term_names):
+        shape = ref_var_info[term][0]
+        exclude_terms[term] = np.zeros(shape)
+
+    return do(model, exclude_terms)
+
+
+def compute_llk(idata, model):
+    """Compute log-likelihood for the submodel."""
+    return compute_log_likelihood(idata, model=model, progressbar=False, extend_inferencedata=False)
+
+
+def get_model_information(model):
+    """
+    Get the size and transformation of each variable in a PyMC model.
+    """
+
+    var_info = {}
+    initial_point = model.initial_point()
+    for v_var in model.value_vars:
+        name = v_var.name
+        if is_transformed_name(name):
+            name = get_untransformed_name(name)
+            x_var = matrix(f"{name}_transformed")
+            z_var = model.rvs_to_transforms[model.values_to_rvs[v_var]].backward(x_var)
+            transformation = function(inputs=[x_var], outputs=z_var)
+        else:
+            transformation = None
+
+        var_info[name] = (
+            initial_point[v_var.name].shape,
+            initial_point[v_var.name].size,
+            transformation,
+        )
+
+    return var_info
diff --git a/kulprit/projection/solver.py b/kulprit/projection/solver.py
index ca71caa..9beff20 100644
--- a/kulprit/projection/solver.py
+++ b/kulprit/projection/solver.py
@@ -1,250 +1,60 @@
 """optimization module."""
-
-# pylint: disable=protected-access
-from typing import List, Optional
 import warnings
 
 import arviz as az
-import bambi as bmb
 import numpy as np
-import xarray as xr
-import preliz as pz
-
 from scipy.optimize import minimize
 
-from kulprit.data.submodel import SubModel
-
-
-class Solver:
-    """The primary solver class, used to perform the projection."""
-
-    def __init__(
-        self,
-        model: bmb.Model,
-        idata: az.InferenceData,
-    ) -> None:
-        """Initialise the main solver object."""
-
-        # log the reference model and inference data objects
-        self.ref_model = model
-        self.ref_idata = idata
-
-        # log the reference model's response name and family
-        self.response_name = self.ref_model.response_component.term.name
-        self.ref_family = self.ref_model.family.name
-
-        # define sampling options
-        self.num_chain = self.ref_idata.posterior.dims["chain"]
-        self.num_samples = self.num_chain * 100
-
-        if self.ref_family not in ["gaussian", "poisson", "bernoulli", "binomial"]:
-            raise NotImplementedError(f"Family {self.ref_family} not supported")
-
-    @property
-    def pps(self):
-        # make in-sample predictions with the reference model if not available
-        if "posterior_predictive" not in self.ref_idata.groups():
-            self.ref_model.predict(self.ref_idata, kind="response", inplace=True)
-
-        pps = az.extract(
-            self.ref_idata,
-            group="posterior_predictive",
-            var_names=[self.response_name],
-            num_samples=self.num_samples,
-        ).values.T
-        return pps
-
-    def _build_bounds(self, init: List[float]) -> list:
-        """Build bounds for the parameters in the optimization.
-
-        This method is used to ensure that dispersion or other auxiliary parameters present
-        in certain families remain within their valid regions.
-
-        Parameters:
-        ----------
-        init : List[float]
-            The list of initial parameter values
-
-        Returns:
-        -------
-        List : [Tuple(float)]
-            The upper and lower bounds for each initialized parameter in the optimization
-        """
-        eps = np.finfo(np.float64).eps
-
-        # build bounds based on family, we are assuming that the last parameter is the dispersion
-        # and that the other parameters are unbounded (like when using Gaussian priors)
-        if self.ref_family in ["gaussian"]:
-            # account for the dispersion parameter
-            bounds = [(None, None)] * (init.size - 1) + [(eps, None)]
-        elif self.ref_family in ["binomial", "poisson", "bernoulli"]:
-            # no dispersion parameter, so no bounds
-            bounds = [(None, None)] * (init.size)
-        return bounds
-
-    def objective(
-        self,
-        params: np.ndarray,
-        obs: np.ndarray,
-        X: Optional[np.ndarray] = None,
-    ) -> np.ndarray:
-        """Variational projection predictive objective function.
-
-        This is negative log-likelihood of the restricted model but evaluated on samples of the
-        posterior predictive distribution of the reference model.
-        Formally, this objective function implements Equation 1 of mean-field projection predictive
-        inference as defined [here](https://www.hackmd.io/@yannmcl/H1CZPjE1i).
-
-        Parameters:
-        ----------
-        params : array_like
-            The optimization parameters mean values
-        obs : array_like
-            One sample from the posterior predictive distribution of the reference model
-        X : array_like
-            The common term design matrix of the submodel
-
-        Returns:
-        -------
-            float: The negative log-likelihood of the reference posterior predictive under the
-        restricted model
-        """
-
-        # Gaussian observation likelihood
-        if self.ref_family == "gaussian":
-            linear_predictor = _linear_predict(beta_x=params[:-1], X=X)
-            neg_llk = pz.Normal(mu=linear_predictor, sigma=params[-1])._neg_logpdf(obs)
-
-        # Binomial observation likelihood
-        elif self.ref_family == "binomial":
-            trials = self.ref_model.response.data[:, 1]
-            linear_predictor = _linear_predict(beta_x=params, X=X)
-            probs = self.ref_model.family.link["p"].linkinv(linear_predictor)
-            neg_llk = pz.Binomial(n=trials, p=probs)._neg_logpdf(obs)
-
-        # Bernoulli observation likelihood
-        elif self.ref_family == "bernoulli":
-            linear_predictor = _linear_predict(beta_x=params, X=X)
-            probs = self.ref_model.family.link["p"].linkinv(linear_predictor)
-            neg_llk = pz.Binomial(p=probs)._neg_logpdf(obs)
 
-        # Poisson observation likelihood
-        elif self.ref_family == "poisson":
-            with warnings.catch_warnings():
-                warnings.filterwarnings("ignore", message="overflow encountered in exp")
-                linear_predictor = _linear_predict(beta_x=params, X=X)
-                lam = self.ref_model.family.link["mu"].linkinv(np.float64(linear_predictor))
-                neg_llk = pz.Poisson(mu=lam)._neg_logpdf(obs)
-
-        return neg_llk
-
-    def solve(self, term_names: List[str], X: np.ndarray, slices: dict) -> SubModel:
-        """The primary projection method in the procedure.
-
-        The projection is performed with a mean-field approximation rather than concatenating
-        posterior draw-wise optimization solutions as is suggested by Piironen (2018).
-        For more information, kindly read https://www.hackmd.io/@yannmcl/H1CZPjE1i.
-
-        Parameters:
-        ----------
-        term_names : List[str]
-            The names of the terms to project onto in the submodel
-        X : array_like
-            The common term design matrix of the submodel
-        slices : dict
-            Slices of the common term design matrix
-
-        Returns:
-        -------
-            SubModel: The projected submodel object
-        """
-        # build the optimization parameter bounds
-        term_values = az.extract(self.ref_idata.posterior, num_samples=self.pps.shape[0])
-        init = np.stack([term_values[key].values.flatten() for key in term_names]).T
-        bounds = self._build_bounds(init[0])
-
-        # perform projection predictive inference
-        res_posterior = []
-        objectives = []
-
-        with warnings.catch_warnings():
-            warnings.filterwarnings("ignore", message="Values in x were outside bounds")
-            for idx, obs in enumerate(self.pps):
-                opt = minimize(
-                    self.objective,
-                    args=(obs, X),
-                    x0=init[idx],
-                    tol=0.001,
-                    bounds=bounds,
-                    method="SLSQP",
-                )
-                res_posterior.append(opt.x)
-                objectives.append(opt.fun)
-
-        # compile the projected posterior
-        res_samples = np.vstack(res_posterior)
-        posterior = {term: res_samples[:, slices[term]] for term in term_names}
-
-        # NOTE: See the draw number is hard-coded. It would be better if we could take it
-        # from a better source.
-        chain_n = len(self.ref_idata.posterior.coords.get("chain"))
-        draw_n = 100  # len(self.ref_idata.posterior.coords.get("draw"))
-
-        # reshape inline with reference model
-        for key, value in posterior.items():
-            new_shape = [chain_n, draw_n]
-            coords_dict = {"chain": np.arange(chain_n), "draw": np.arange(draw_n)}
-
-            parma_coords = self.ref_idata.posterior[key].coords
-            param_dims = self.ref_idata.posterior[key].dims
-            extra_dims = tuple(dim for dim in param_dims if dim not in ["chain", "draw"])
-
-            for dim in extra_dims:
-                param_coord = parma_coords.get(dim)
-                coords_dict[dim] = param_coord
-                new_shape.append(len(param_coord))
-
-            # NOTE I'm not sure if this is doing the right thing. We should double check it.
-            value = value.reshape(new_shape)
-            posterior[key] = xr.DataArray(value, coords=coords_dict)
-
-        posterior = xr.Dataset(posterior)
-
-        # compute the average loss
-        loss = np.mean(objectives)
-        return posterior, loss
-
-
-def _linear_predict(
-    beta_x: np.ndarray,
-    X: Optional[np.ndarray] = None,
-) -> np.ndarray:
-    """Predict the latent predictor of the submodel.
+def solve(model_log_likelihood, pps, initial_guess, var_info):
+    """The primary projection method in the procedure.
 
     Parameters:
     ----------
-    beta_x : np.ndarray
-        The model's projected posterior
-    X : np.ndarray
-        The model's common design matrix
+    model_log_likelihood: Callable
+        The log-likelihood function of the model
+    pps: array
+        The predictions of the reference model
+    initial_guess: array
+        The initial guess for the optimization
+    var_info: dict
+        The dictionary containing information about the size and transformation of the variables
 
     Returns:
     -------
-    np.ndarray: Point estimate of the latent predictor using the single draw from the
-    posterior and the model's design matrix
+        new_idata: arviz.InferenceData
+        loss: float
     """
-
-    linear_predictor = np.zeros(shape=(X.shape[0],))
-
-    # Contribution due to common terms
-    if X is not None:
-
-        if len(beta_x.shape) > 1:
-            raise NotImplementedError("Currently this method only works for single samples.")
-
-        # 'contribution' is of shape * (obs_n, ) for univariate
-        contribution = np.dot(X, beta_x.T).T
-        linear_predictor += contribution
-
-    # return the latent predictor
-    return linear_predictor
+    num_samples = len(pps)
+    posterior_array = np.zeros((num_samples, len(initial_guess)))
+    posterior_dict = {}
+    objectives = []
+
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", message="Values in x were outside bounds")
+        for idx, obs in enumerate(pps):
+            opt = minimize(
+                model_log_likelihood,
+                args=(obs),
+                x0=initial_guess,
+                tol=0.001,
+                method="SLSQP",
+            )
+
+            posterior_array[idx] = opt.x
+            objectives.append(opt.fun)
+            initial_guess = opt.x
+
+    size = 0
+    for key, values in var_info.items():
+        shape, new_size, transformation = values
+        posterior_dict[key] = posterior_array[:, size : size + new_size].reshape(
+            1, num_samples, *shape
+        )
+        if transformation is not None:
+            posterior_dict[key] = transformation(posterior_dict[key])
+        size += new_size
+
+    new_idata = az.from_dict(posterior=posterior_dict)
+    loss = np.mean(objectives)
+    return new_idata, loss
diff --git a/kulprit/reference.py b/kulprit/reference.py
index 6a02995..7f0f9ec 100644
--- a/kulprit/reference.py
+++ b/kulprit/reference.py
@@ -13,7 +13,8 @@
 from kulprit.data.submodel import SubModel
 from kulprit.plots.plots import plot_compare, plot_densities
 from kulprit.projection.projector import Projector
-from kulprit.search.searcher import Searcher
+from kulprit.search.forward import ForwardSearchPath
+from kulprit.search.l1 import L1SearchPath
 
 
 class ProjectionPredictive:
@@ -25,6 +26,7 @@ def __init__(
         self,
         model: bmb.models.Model,
         idata: Optional[az.InferenceData] = None,
+        num_samples: int = 400,
     ) -> None:
         """Builder for projection predictive model selection.
 
@@ -39,7 +41,8 @@ def __init__(
             The ArviZ InferenceData object of the fitted reference model
         """
         # test that the reference model has an intercept term
-        if not bmb.formula.formula_has_intercept(model.formula.main):
+        self.has_intercept = bmb.formula.formula_has_intercept(model.formula.main)
+        if not self.has_intercept:
             raise UserWarning(
                 "The procedure currently requires reference models to have an intercept term."
             )
@@ -65,9 +68,17 @@ def __init__(
         self.model = model
         self.idata = idata
 
+        self.searcher_path = None
+        # indicator variable tracking whether or not a search has been run
+        self.search_completed = False
+
         # instantiate projector, searcher classes
-        self.projector = Projector(model=self.model, idata=self.idata)
-        self.searcher = Searcher(self.projector)
+        self.projector = Projector(
+            model=self.model,
+            idata=self.idata,
+            num_samples=num_samples,
+            has_intercept=self.has_intercept,
+        )
         # we have not yet run a search
         self.path = None
 
@@ -76,17 +87,12 @@ def __repr__(self) -> str:
 
         # return the formular for the reference model if no search has been run
         if self.path is None:
-            return (
-                f"ReferenceModel("
-                f"{', '.join([self.model.formula.main] + list(self.model.formula.additionals))})"
-            )
+            return "ReferenceModel"
 
         # otherwise return the formulas for the submodels
         else:
             str_of_submodels = "\n".join(
-                f"{idx:>3} "
-                f"{', '.join([value.model.formula.main] + list(value.model.formula.additionals))}"
-                for idx, value in enumerate(self.path.values())
+                f"{idx:>3} " f"{value}" for idx, value in enumerate(self.path.values())
             )
             return str_of_submodels
 
@@ -136,6 +142,16 @@ def search(
             search path, keyed by their model size.
         """
 
+        # test valid solution method
+        if method not in ["forward", "l1"]:
+            raise ValueError("Please select either forward search or L1 search.")
+
+        # initialise search path
+        if method == "forward":
+            self.searcher_path = ForwardSearchPath(self.projector)
+        else:
+            self.searcher_path = L1SearchPath(self.projector)
+
         # set default `max_terms` value
         n_terms = len(self.model.components[self.model.family.likelihood.parent].common_terms)
         if max_terms is None:
@@ -148,15 +164,19 @@ def search(
                 + "reference model."
             )
 
-        # perform search through the parameter space
-        self.path = self.searcher.search(
-            max_terms=max_terms,
-            method=method,
-        )
+        # perform the search according to the chosen heuristic
+        k_submodels = self.searcher_path.search(max_terms=max_terms)
+
+        # feed path result through to the projector
+        self.projector.path = k_submodels
+        self.path = k_submodels
+        # toggle indicator variable and return search path
+        self.search_completed = True
 
-    def plot_compare(
+    def compare(
         self,
-        plot: Optional[bool] = False,
+        plot: Optional[bool] = True,
+        min_model_size: Optional[int] = 0,
         legend: Optional[bool] = True,
         title: Optional[bool] = True,
         figsize: Optional[tuple] = None,
@@ -168,7 +188,7 @@ def plot_compare(
         Parameters:
         -----------
         plot : bool
-            Plot the results of the comparison. Defaults to False
+            Plot the results of the comparison. Defaults to True
         legend : bool
             Add legend to figure. Defaults to True.
         title : bool
@@ -204,24 +224,25 @@ def plot_compare(
         """
 
         # test that search has been previously run
-        if self.searcher.search_completed is False:
+        if self.search_completed is False:
             raise UserWarning("Please run search before comparing submodels.")
 
         # initiate plotting arguments if none provided
         if plot_kwargs is None:
             plot_kwargs = {}
 
-        self.searcher.idatas = {}  # pylint: disable=attribute-defined-outside-init
+        self.searcher_idatas = {}  # pylint: disable=attribute-defined-outside-init
 
         # make dictionary of inferencedata objects for each projection
-        for k, submodel in self.searcher.path.k_submodel.items():
-            self.searcher.idatas[k] = submodel.idata
-        self.searcher.idatas[
+        for k, submodel in self.searcher_path.k_submodel.items():
+            if k >= min_model_size:
+                self.searcher_idatas[k] = submodel.idata
+        self.searcher_idatas[
             k + 1  # pylint: disable=undefined-loop-variable
-        ] = self.searcher.projector.idata
+        ] = self.projector.idata
 
         # compare the submodels using loo (other criteria may be added in the future)
-        comparison = az.compare(self.searcher.idatas)
+        comparison = az.compare(self.searcher_idatas)
         comparison.sort_index(ascending=False, inplace=True)
 
         # plot the comparison if requested
diff --git a/kulprit/search/forward.py b/kulprit/search/forward.py
index a83f14e..50ebbce 100644
--- a/kulprit/search/forward.py
+++ b/kulprit/search/forward.py
@@ -4,12 +4,11 @@
 
 import pandas as pd
 from kulprit.data.submodel import SubModel
-from kulprit.projection.projector import Projector
 from kulprit.search import SearchPath
 
 
 class ForwardSearchPath(SearchPath):
-    def __init__(self, projector: Projector) -> None:
+    def __init__(self, projector) -> None:
         """Initialise search path class."""
 
         # log the projector object
diff --git a/kulprit/search/searcher.py b/kulprit/search/searcher.py
deleted file mode 100644
index 153aa1b..0000000
--- a/kulprit/search/searcher.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""Core search module."""
-
-from typing_extensions import Literal
-
-from kulprit.projection.projector import Projector
-from kulprit.search.forward import ForwardSearchPath
-from kulprit.search.l1 import L1SearchPath
-
-
-class Searcher:
-    def __init__(self, projector: Projector) -> None:
-        """Initialise forward or l1 search class.
-
-        Parameters:
-        ----------
-        projector : Projector
-            A projector object.
-
-        Raises
-        ------
-            UserWarning: If method is not "forward" or "l1".
-        """
-        # initialise projector for the search procedure
-        self.projector = projector
-
-        # define all available search heuristics
-        self.method_dict = {
-            "forward": ForwardSearchPath,
-            "l1": L1SearchPath,
-        }
-
-        # indicator variable tracking whether or not a search has been run
-        self.search_completed = False
-
-    def __repr__(self) -> str:
-        """String representation of the search path."""
-
-        return self.path.__repr__()
-
-    def search(self, max_terms: int, method: Literal["forward", "l1"] = "forward") -> dict:
-        """Primary search method of the procedure.
-
-        Performs forward search through the parameter space.
-
-        Parameters:
-        ----------
-        max_terms : int
-            Number of terms to perform the forward search up to.
-        method : str
-            Method to use for search.
-
-        Returns:
-        -------
-        dict: A dictionary of submodels, keyed by the number of terms in the submodel.
-        """
-
-        # test valid solution method
-        if method not in self.method_dict:
-            raise UserWarning("Please either select either forward search or L1 search.")
-
-        # initialise search path
-        self.path = self.method_dict[method](  # pylint: disable=attribute-defined-outside-init
-            self.projector
-        )
-
-        # perform the search according to the chosen heuristic
-        k_submodels = self.path.search(max_terms=max_terms)
-
-        # toggle indicator variable and return search path
-        self.search_completed = True
-
-        # feed path result through to the projector
-        self.projector.path = k_submodels
-
-        # return the final search path
-        return k_submodels
diff --git a/pyproject.toml b/pyproject.toml
index b2cadfe..d6b60cc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -27,9 +27,7 @@ dependencies = [
     "arviz>=0.17.1",
     "bambi>=0.12.0",
     "scikit-learn>=1.0.2",
-    "numba>=0.56.0",
-    "preliz>=0.4.1"
-]
+    "numba>=0.56.0",]
 
 [tool.flit.module]
 name = "kulprit"
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 25fb4c0..00bb7d3 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,5 +1,5 @@
 black==22.3.0
-pylint==2.17.4
+pylint==3.1.0
 pytest-cov>=2.6.1
 pytest>=4.4.0
 pre-commit>=2.19


Test patch:
diff --git a/tests/conftest.py b/tests/conftest.py
index acc9571..f3c8e6b 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -21,6 +21,15 @@ def bambi_model():  # pragma: no cover
     return model
 
 
+@pytest.fixture(scope="session")
+def pymc_model(bambi_model):  # pragma: no cover
+    """Return a bambi model."""
+
+    bambi_model.build()
+    model = bambi_model.backend.model
+    return model
+
+
 @pytest.fixture(scope="session")
 def bambi_model_idata(bambi_model):  # pragma: no cover
     """Return a bambi model fitted inference data."""
diff --git a/tests/test_project.py b/tests/test_project.py
index 873d68d..ba8be98 100644
--- a/tests/test_project.py
+++ b/tests/test_project.py
@@ -1,4 +1,3 @@
-# pylint: disable=no-self-use
 import copy
 import pytest
 
@@ -12,9 +11,9 @@
 
 
 class TestProjector(KulpritTest):
-    """Test projection methods in the procedure."""
+    """Test projection methods."""
 
-    NUM_DRAWS, NUM_CHAINS = 500, 4
+    NUM_CHAINS, NUM_DRAWS = 4, 500
 
     def test_idata_is_none(self, bambi_model):
         """Test that some inference data is automatically produced when None."""
@@ -47,23 +46,6 @@ def test_hierarchical_error(self):
             # build a bad reference model object
             kpt.ProjectionPredictive(bad_model)
 
-    def test_unimplemented_family(self):
-        """Test that an error is raised when an unimplemented family is used."""
-
-        # define model data
-        data = bmb.load_data("my_data")
-        # define model
-        bad_model = bmb.Model("z ~ x + y", data, family="t")
-        bad_idata = bad_model.fit(
-            draws=self.NUM_DRAWS,
-            chains=self.NUM_CHAINS,
-            idata_kwargs={"log_likelihood": True},
-        )
-
-        with pytest.raises(NotImplementedError):
-            # build a bad reference model object
-            kpt.ProjectionPredictive(bad_model, bad_idata)
-
     def test_different_variate_name(self, bambi_model_idata):
         """Test that an error is raised when model and idata aren't compatible."""
 
@@ -159,28 +141,3 @@ def test_project_wrong_term_names(self, ref_model):
         with pytest.raises(UserWarning):
             # project the reference model to some parameter subset
             ref_model.project(terms=["spam", "ham"])
-
-    def test_build_restricted_model(self, bambi_model, bambi_model_idata):
-        """Test that restricted model building works as expected."""
-
-        # build restricted model which is the same as the reference model
-        solver = kpt.projection.projector.Projector(model=bambi_model, idata=bambi_model_idata)
-        new_model = solver._build_restricted_model(["x", "y"])  # pylint: disable=protected-access
-
-        # perform checks
-        assert new_model.formula.__str__() == bambi_model.formula.__str__()
-        assert new_model.data.shape == bambi_model.data.shape
-        assert np.all(
-            new_model.data.loc[:, new_model.data.columns != solver.response_name]
-            == bambi_model.data.loc[:, new_model.data.columns != solver.response_name]
-        )
-        assert new_model.family.name == bambi_model.family.name
-        assert (
-            new_model.components[new_model.family.likelihood.parent].common_terms.keys()
-            == bambi_model.components[bambi_model.family.likelihood.parent].common_terms.keys()
-        )
-
-        assert set(
-            new_model.components[new_model.family.likelihood.parent].common_terms.keys()
-        ) == set(bambi_model.components[bambi_model.family.likelihood.parent].common_terms.keys())
-        assert new_model.response_component.term.name == bambi_model.response_component.term.name
diff --git a/tests/test_pymc_io.py b/tests/test_pymc_io.py
new file mode 100644
index 0000000..2d65901
--- /dev/null
+++ b/tests/test_pymc_io.py
@@ -0,0 +1,27 @@
+from kulprit.projection.pymc_io import compile_mllk, compute_new_model, get_model_information
+from tests import KulpritTest
+
+
+class TestProjector(KulpritTest):
+    """Test projection methods."""
+
+    def test_compile_mllk(self, pymc_model):
+        fmodel, old_y_value, obs_rvs = compile_mllk(pymc_model)
+        assert callable(fmodel)
+        assert old_y_value is not None
+        assert obs_rvs is not None
+
+    def test_compute_new_model(self, pymc_model):
+        all_terms = [fvar.name for fvar in pymc_model.free_RVs]
+        var_info = get_model_information(pymc_model)
+        term_names = all_terms[-1:]
+        new_model = compute_new_model(pymc_model, var_info, all_terms, term_names)
+        assert new_model is not None
+
+    def test_get_model_information(self, pymc_model):
+        var_info = get_model_information(pymc_model)
+        assert "x" in var_info
+        assert isinstance(var_info["x"], tuple)
+        assert len(var_info["x"]) == 3
+        assert isinstance(var_info["x"][1], int)
+        assert var_info["x"][2] is None
diff --git a/tests/test_search.py b/tests/test_search.py
index c1c2d1e..39d671f 100644
--- a/tests/test_search.py
+++ b/tests/test_search.py
@@ -1,4 +1,3 @@
-# pylint: disable=no-self-use
 import copy
 import pytest
 
@@ -12,7 +11,7 @@
 class TestSearch:
     """Test the search method of the model selection procedure."""
 
-    NUM_DRAWS, NUM_CHAINS = 50, 2
+    NUM_CHAINS, NUM_DRAWS = 2, 50
 
     def test_forward(self, ref_model):
         """Test that the search path is as expected."""
@@ -54,7 +53,7 @@ def test_l1_categorical_error(self):
     def test_bad_search_method(self, ref_model):
         """Test that an error is raised when an invalid search method is used."""
 
-        with pytest.raises(UserWarning):
+        with pytest.raises(ValueError):
             ref_model_copy = copy.copy(ref_model)
             ref_model_copy.search(method="bad_method")
 
@@ -71,7 +70,7 @@ def test_loo(self, ref_model):
 
         ref_model_copy = copy.copy(ref_model)
         ref_model_copy.search()
-        cmp, _ = ref_model_copy.plot_compare()
+        cmp, _ = ref_model_copy.compare()
         all(cmp.index == [0, 1, 2, 3])
 
     def test_loo_with_no_search_path(self, ref_model):
@@ -84,30 +83,28 @@ def test_loo_with_no_search_path(self, ref_model):
             bambi_model = bmb.Model("z ~ x + y", data, family="gaussian")
             idata = bambi_model.fit(draws=self.NUM_DRAWS, chains=self.NUM_CHAINS)
             ref_model = kpt.ProjectionPredictive(model=bambi_model, idata=idata)
-            ref_model.plot_compare()
+            ref_model.compare()
 
     def test_forward_repr(self, ref_model):
         """Test the string representation of the forward search path."""
 
         ref_model_copy = copy.copy(ref_model)
         path = ref_model_copy.search(method="forward")
-        assert isinstance(ref_model_copy.searcher.__repr__(), str)
-        assert isinstance(path.__repr__(), str)
+        assert isinstance(repr(path), str)
 
     def test_l1_repr(self, ref_model):
         """Test the string representation of the L1 search path."""
 
         ref_model_copy = copy.copy(ref_model)
         path = ref_model_copy.search(method="l1")
-        assert isinstance(ref_model_copy.searcher.__repr__(), str)
-        assert isinstance(path.__repr__(), str)
+        assert isinstance(repr(path), str)
 
     def test_plot_comparison(self, ref_model):
         """Test the LOO compare plotting method."""
 
         ref_model_copy = copy.copy(ref_model)
         ref_model_copy.search()
-        ref_model_copy.plot_compare(plot=True, figsize=(10, 5))
+        ref_model_copy.compare(plot=False, figsize=(10, 5), min_model_size=1)
 
     @pytest.mark.parametrize(
         "kwargs",