diff --git a/examples/framingham_final_experiment.ipynb b/examples/framingham_final_experiment.ipynb
new file mode 100644
index 0000000..d2ccfa6
--- /dev/null
+++ b/examples/framingham_final_experiment.ipynb
@@ -0,0 +1,856 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dsm import datasets, DeepRecurrentSurvivalMachines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def unrollx(data):\n",
+    "     return np.vstack([dat for dat in data])\n",
+    "def unrollt(data):\n",
+    "     return np.concatenate([dat for dat in data])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, t, e = datasets.load_dataset('FRAMINGHAM', sequential=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, t, e = np.array(x), np.array(t), np.array(e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "times = np.quantile(unrollt(t)[unrollt(e)==1], [0.25, .5, 0.75])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2254.5, 3913. , 5766. ])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "folds = np.array((list(range(4))*10000)[:len(x)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([8766, 4138, 8766, ..., 8766, 6376, 4565], dtype=int64)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unrollt(t)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lifelines import CoxPHFitter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert_to_data_frame(x, t, e):\n",
+    "\n",
+    "  df = pd.DataFrame(data=x, columns=['X' + str(i) for i in range(x.shape[1])])\n",
+    "  df['T'] = pd.DataFrame(data=t.reshape(-1, 1), columns=['T'])\n",
+    "  df['E'] = pd.DataFrame(data=e.reshape(-1, 1), columns=['E'])\n",
+    "\n",
+    "  return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sksurv.metrics import concordance_index_ipcw, brier_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2897, 3)\n",
+      "(2922, 3)\n",
+      "(2919, 3)\n",
+      "(2889, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "cis = []\n",
+    "brs = []\n",
+    "\n",
+    "for fold in set(folds):  \n",
+    "    \n",
+    "    x_tr, t_tr, e_tr = x[folds!=fold], t[folds!=fold], e[folds!=fold]\n",
+    "    x_te, t_te, e_te = x[folds==fold], t[folds==fold], e[folds==fold]\n",
+    "    \n",
+    "    x_tr = unrollx(x_tr)\n",
+    "    t_tr = unrollt(t_tr)\n",
+    "    e_tr = unrollt(e_tr)\n",
+    "\n",
+    "    x_te = unrollx(x_te)\n",
+    "    t_te = unrollt(t_te)\n",
+    "    e_te = unrollt(e_te)\n",
+    "    \n",
+    "    df_tr = convert_to_data_frame(x_tr, t_tr, e_tr)\n",
+    "\n",
+    "    model = CoxPHFitter(penalizer=1e-3).fit(df_tr, duration_col='T', event_col='E')\n",
+    "    \n",
+    "    preds = model.predict_survival_function(x_te, times).T.values\n",
+    "    \n",
+    "    et_tr = np.array([(e_tr[i], t_tr[i]) for i in range(len(e_tr))],\n",
+    "                 dtype=[('e', bool), ('t', int)])\n",
+    "    et_te = np.array([(e_te[i], t_te[i]) for i in range(len(e_te))],\n",
+    "                 dtype=[('e', bool), ('t', int)])\n",
+    "    \n",
+    "    print (preds.shape)\n",
+    "    \n",
+    "    cis_ = []\n",
+    "    for i in range(len(times)):\n",
+    "        cis_.append(concordance_index_ipcw(et_tr, et_te, 1-preds[:,i], times[i])[0])\n",
+    "    cis.append(cis_)\n",
+    "    \n",
+    "    brs.append(brier_score(et_tr, et_te, preds, times )[1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.76853118 0.76241752 0.76215845]\n",
+      "[0.06231811 0.10537517 0.13165702]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print (np.mean(cis, axis=0))\n",
+    "print (np.mean(brs, axis=0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pysurvival.models.survival_forest import RandomSurvivalForestModel\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cis = []\n",
+    "brs = []\n",
+    "\n",
+    "for fold in set(folds):  \n",
+    "    \n",
+    "    x_tr, t_tr, e_tr = x[folds!=fold], t[folds!=fold], e[folds!=fold]\n",
+    "    x_te, t_te, e_te = x[folds==fold], t[folds==fold], e[folds==fold]\n",
+    "    \n",
+    "    x_tr = unrollx(x_tr)\n",
+    "    t_tr = unrollt(t_tr)\n",
+    "    e_tr = unrollt(e_tr)\n",
+    "\n",
+    "    x_te = unrollx(x_te)\n",
+    "    t_te = unrollt(t_te)\n",
+    "    e_te = unrollt(e_te)\n",
+    "    \n",
+    "    df_tr = convert_to_data_frame(x_tr, t_tr, e_tr)\n",
+    "    \n",
+    "        \n",
+    "    et_tr = np.array([(e_tr[i], t_tr[i]) for i in range(len(e_tr))],\n",
+    "                 dtype=[('e', bool), ('t', int)])\n",
+    "    et_te = np.array([(e_te[i], t_te[i]) for i in range(len(e_te))],\n",
+    "                 dtype=[('e', bool), ('t', int)])\n",
+    "    \n",
+    "    model = RandomSurvivalForestModel(num_trees=50)\n",
+    "    model = model.fit(x_tr, t_tr, e_tr)\n",
+    "    \n",
+    "    preds = []\n",
+    "    for time in times:\n",
+    "        preds.append(model.predict_survival(x_te, time))\n",
+    "    print (len(preds))\n",
+    "    \n",
+    "    cis_ = []\n",
+    "    for i in range(len(times)):\n",
+    "        cis_.append(concordance_index_ipcw(et_tr, et_te, 1-preds[:,i], times[i])[0])\n",
+    "    cis.append(cis_)\n",
+    "    \n",
+    "    brs.append(brier_score(et_tr, et_te, preds, times )[1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.predict_survival_function(x_te, times).T.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       " 0.9088778045841828,\n",
+       " 0.6112634538501532,\n",
+       " 0.29206769385932985]"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[risk.y[3] for risk in out_risk]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/pbc_final_experiment.ipynb b/examples/pbc_final_experiment.ipynb
new file mode 100644
index 0000000..d3428e9
--- /dev/null
+++ b/examples/pbc_final_experiment.ipynb
@@ -0,0 +1,816 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dsm import datasets, DeepRecurrentSurvivalMachines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, t, e = datasets.load_dataset('PBC', sequential=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, t, e = np.array(x), np.array(t), np.array(e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "times = np.quantile(unrollt(t), [0.25, .5, 0.75])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2.1054649 , 4.57507392, 7.15967583])"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "folds = np.array((list(range(4))*1000)[:len(x)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def unrollx(data):\n",
+    "     return np.vstack([dat for dat in data])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def unrollt(data):\n",
+    "     return np.concatenate([dat for dat in data])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.0951703 ,  0.56948856, 14.15233819, ...,  2.92136677,\n",
+       "        1.86726536,  1.04588764])"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unrollt(t)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lifelines import CoxPHFitter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert_to_data_frame(x, t, e):\n",
+    "\n",
+    "  df = pd.DataFrame(data=x, columns=['X' + str(i) for i in range(x.shape[1])])\n",
+    "  df['T'] = pd.DataFrame(data=t.reshape(-1, 1), columns=['T'])\n",
+    "  df['E'] = pd.DataFrame(data=e.reshape(-1, 1), columns=['E'])\n",
+    "\n",
+    "  return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sksurv.metrics import concordance_index_ipcw, brier_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(478, 3)\n",
+      "(505, 3)\n",
+      "(457, 3)\n",
+      "(505, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "cis = []\n",
+    "brs = []\n",
+    "\n",
+    "for fold in set(folds):  \n",
+    "    \n",
+    "    x_tr, t_tr, e_tr = x[folds!=fold], t[folds!=fold], e[folds!=fold]\n",
+    "    x_te, t_te, e_te = x[folds==fold], t[folds==fold], e[folds==fold]\n",
+    "    \n",
+    "    x_tr = unrollx(x_tr)\n",
+    "    t_tr = unrollt(t_tr)\n",
+    "    e_tr = unrollt(e_tr)\n",
+    "\n",
+    "    x_te = unrollx(x_te)\n",
+    "    t_te = unrollt(t_te)\n",
+    "    e_te = unrollt(e_te)\n",
+    "    \n",
+    "    df_tr = convert_to_data_frame(x_tr, t_tr, e_tr)\n",
+    "\n",
+    "    model = CoxPHFitter(penalizer=1e-3).fit(df_tr, duration_col='T', event_col='E')\n",
+    "    \n",
+    "    preds = model.predict_survival_function(x_te, times).T.values\n",
+    "    \n",
+    "    et_tr = np.array([(e_tr[i], t_tr[i]) for i in range(len(e_tr))],\n",
+    "                 dtype=[('e', bool), ('t', int)])\n",
+    "    et_te = np.array([(e_te[i], t_te[i]) for i in range(len(e_te))],\n",
+    "                 dtype=[('e', bool), ('t', int)])\n",
+    "    \n",
+    "    print (preds.shape)\n",
+    "    \n",
+    "    cis_ = []\n",
+    "    for i in range(len(times)):\n",
+    "        cis_.append(concordance_index_ipcw(et_tr, et_te, 1-preds[:,i], times[i])[0])\n",
+    "    cis.append(cis_)\n",
+    "    \n",
+    "    brs.append(brier_score(et_tr, et_te, preds, times )[1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[0.8622349652479977, 0.8496178660113483, 0.8359660027234909],\n",
+       " [0.9012252214624602, 0.8471243525992452, 0.7944140040816455],\n",
+       " [0.8467573397188136, 0.8212682235979585, 0.7283492496826471],\n",
+       " [0.896278572134772, 0.8628305222741215, 0.7505466410210001]]"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.60925486, 0.23441728, 0.04069175],\n",
+       "       [0.55294913, 0.17648165, 0.0217467 ],\n",
+       "       [0.74530162, 0.42290859, 0.14965458],\n",
+       "       ...,\n",
+       "       [0.9551488 , 0.87429316, 0.7434154 ],\n",
+       "       [0.74180704, 0.41712966, 0.14517828],\n",
+       "       [0.3992932 , 0.06804004, 0.00265338]])"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.predict_survival_function(x_te, times).T.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.3126132634036184,\n",
+       " 0.46647488732207254,\n",
+       " 0.5624185126209059,\n",
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+       " 0.2670742533214408,\n",
+       " 0.06359725383083349,\n",
+       " 0.9349522484067815,\n",
+       " 0.9486941636172256,\n",
+       " 0.9522407447171799,\n",
+       " 0.9103188179868816,\n",
+       " 0.8824124615716761,\n",
+       " 0.7801756137064368,\n",
+       " 0.752123603797489,\n",
+       " 0.5288001066166764,\n",
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+       " 0.46932603202272666,\n",
+       " 0.9289505993097185,\n",
+       " 0.9355269013394406,\n",
+       " 0.9575783226069599,\n",
+       " 0.9381858563916292,\n",
+       " 0.9595487184435972,\n",
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+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[risk.y[3] for risk in out_risk]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}