diff --git a/README.md b/README.md
index 2a9938e..2f61760 100644
--- a/README.md
+++ b/README.md
@@ -81,6 +81,7 @@ The package somes with a suite of examples on real data:
 - Plankton Counting with Computer Vision (label shift) ([```plankton.ipynb```](https://github.com/aangelopoulos/ppi_py/blob/main/examples/plankton.ipynb))
 - Ballot Counting with Computer Vision ([```ballots.ipynb```](https://github.com/aangelopoulos/ppi_py/blob/main/examples/ballots.ipynb))
 - Income Analysis with Boosting Trees ([```census_income.ipynb```](https://github.com/aangelopoulos/ppi_py/blob/main/examples/census_income.ipynb))
+- Tree Cover Analysis with Computer Vision (Predict-Then-Debias) ([```tree_cover_ptd.ipynb```](https://github.com/aangelopoulos/ppi_py/blob/main/examples/tree_cover_ptd.ipynb))
 
 # Usage and Documentation
 There is a common template that all PPI confidence intervals follow.
@@ -143,4 +144,6 @@ The repository currently implements the methods developed in the following paper
 
 [Prediction-Powered Bootstrap](https://arxiv.org/abs/2405.18379)
 
+[Prediction-Powered Inference with Imputed Covariates and Nonuniform Sampling](https://arxiv.org/abs/2501.18577)
+
 [The Mixed Subjects Design: Treating Large Language Models as Potentially Informative Observations](https://doi.org/10.1177/00491241251326865)
\ No newline at end of file
diff --git a/examples/README.md b/examples/README.md
index 3ff9e3d..2541308 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -17,4 +17,6 @@ Each notebook runs a simulation that forms a dataframe containing confidence int
 
 Each notebook also compares PPI and classical inference in terms of the number of labeled examples needed to reject a natural null hypothesis in the analyzed problem.
 
+The notebook [```tree_cover_ptd.ipynb```](https://github.com/aangelopoulos/ppi_py/blob/main/examples/tree_cover_ptd.ipynb) shows how to use the Predict-Then-Debias (PTD) estimator from Kluger et al. (2025), 'Prediction-Powered Inference with Imputed Covariates and Nonuniform Sampling,' https://arxiv.org/abs/2501.18577.
+
 Finally, there is a notebook that shows how to compute the optimal `n` and `N` given a cost constraint ([```power_analysis.ipynb```](https://github.com/aangelopoulos/ppi_py/blob/main/examples/power_analysis.ipynb)).
\ No newline at end of file
diff --git a/examples/power_analysis.ipynb b/examples/power_analysis.ipynb
index 09fd36c..9e0106a 100644
--- a/examples/power_analysis.ipynb
+++ b/examples/power_analysis.ipynb
@@ -949,7 +949,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -963,7 +963,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,
diff --git a/examples/tree_cover_ptd.ipynb b/examples/tree_cover_ptd.ipynb
new file mode 100644
index 0000000..afadc12
--- /dev/null
+++ b/examples/tree_cover_ptd.ipynb
@@ -0,0 +1,1651 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cc8a7fc7",
+   "metadata": {},
+   "source": [
+    "The **ptd** module of the **ppi_py** package implements the Predict-then-Debias (PTD) bootstrap algorithm from Kluger et al. (2025), 'Prediction-Powered Inference with Imputed Covariates and Nonuniform Sampling,' <https://arxiv.org/abs/2501.18577>. The algorithm takes in an estimator of interest, a large \"unlabeled\" dataset of machine learning predictions, and a small \"calibration\" dataset of ground truth measurements. It outputs valid point estimates and confidence intervals for the estimator of interest.  \n",
+    "\n",
+    "In this notebook, we demonstrate how to use the ptd module to estimate linear regression and logistic regression coefficients. We also show how users can use the module on their own estimators of interest. \n",
+    "\n",
+    "We compare results from PTD with the \"classical\" estimator (using calibration ground truth points only) and \"naive\" estimator (using predictions only). By combining ground truth and predicted datasets, PTD produces statistically valid confidence intervals that are narrower than those for the classical estimator."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1378ab65",
+   "metadata": {},
+   "source": [
+    "# Import packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7e74ca93",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, sys\n",
+    "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), os.pardir)))\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pickle\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels\n",
+    "\n",
+    "from ppi_py.datasets.datasets import load_dataset\n",
+    "from ppi_py import ptd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "636308ff",
+   "metadata": {},
+   "source": [
+    "# Baseline methods for comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bdf67778",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from statsmodels.regression.linear_model import WLS, RegressionResults\n",
+    "from statsmodels.stats.weightstats import _zconfint_generic\n",
+    "from statsmodels.genmod.generalized_linear_model import GLM\n",
+    "from statsmodels.genmod.families import Binomial\n",
+    "from statsmodels.genmod.families.links import Logit\n",
+    "\n",
+    "def classical_logistic_regression_ci(X, Y, w=None, alpha=0.05):\n",
+    "    \"\"\"\n",
+    "    Computes confidence intervals for logistic regression coefficients using the classical method.\n",
+    "\n",
+    "    Args:\n",
+    "        X (ndarray): labeled covariates (dimensions n x p)\n",
+    "        Y (ndarray): labeled responses (length n)\n",
+    "        w (ndarray, optional): sample weights for the labeled dataset (length n)\n",
+    "        alpha (float, optional): error level (must be in the range (0, 1)). Confidence interval will target a coverage of 1 - alpha.\n",
+    "\n",
+    "    Returns:\n",
+    "        tuple: lower and upper bounds of classical confidence intervals for the coefficients\n",
+    "    \"\"\"\n",
+    "    regression = GLM(endog=Y, exog=X, freq_weights=w, family=Binomial(link=Logit())).fit()\n",
+    "    ci = regression.conf_int(alpha=alpha).T\n",
+    "    return ci\n",
+    "\n",
+    "def classical_linear_regression_ci(X, Y, w=None, alpha=0.05):\n",
+    "    \"\"\"\n",
+    "    Computes confidence intervals for linear regression coefficients using the classical method.\n",
+    "\n",
+    "    Args:\n",
+    "        X (ndarray): labeled covariates (dimensions n x p)\n",
+    "        Y (ndarray): labeled responses (length n)\n",
+    "        w (ndarray, optional): sample weights for the labeled dataset (length n)\n",
+    "        alpha (float, optional): error level (must be in the range (0, 1)). Confidence interval will target a coverage of 1 - alpha.\n",
+    "\n",
+    "    Returns:\n",
+    "        tuple: lower and upper bounds of classical confidence intervals for the coefficients\n",
+    "    \"\"\"\n",
+    "    if w is None:\n",
+    "        regression = WLS(endog=Y, exog=X).fit()\n",
+    "    else:\n",
+    "        regression = WLS(endog=Y, exog=X, weights=w).fit()\n",
+    "    coeff = regression.params\n",
+    "    se = regression.HC0_se\n",
+    "    ci = _zconfint_generic(coeff, se, alpha, alternative=\"two-sided\")\n",
+    "    return (ci[0], ci[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14ad556b",
+   "metadata": {},
+   "source": [
+    "# Load dataset (tree cover, elevation, population)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ca7f640",
+   "metadata": {},
+   "source": [
+    "We use the MOSAIKS dataset from (Rolf et al, 2021). The dataset contains both ground truth and predicted values for tree cover, elevation, and population variables in the contiguous United States.\n",
+    "\n",
+    "After dropping points with missing variables, there are N=67968 total points. For each of our experiments, we will use a uniformly random sampled subset of the points as a calibration dataset (with ground truth and predicted values available), and use the rest as an unlabeled dataset (with predicted values only).\n",
+    "\n",
+    "E. Rolf, J. Proctor, T. Carleton, I. Bolliger, V. Shankar, M. Ishihara, B. Recht, and S. Hsiang. \"A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery,\" Nature Communications, 2021. https://github.com/Global-Policy-Lab/mosaiks-paper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "57c898ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset tree_cover not found at location ./data/; downloading now...\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\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>truth_tree</th>\n",
+       "      <th>preds_tree</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>truth_elevation</th>\n",
+       "      <th>preds_elevation</th>\n",
+       "      <th>truth_population</th>\n",
+       "      <th>preds_population</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-119.794019</td>\n",
+       "      <td>46.773157</td>\n",
+       "      <td>0.423914</td>\n",
+       "      <td>0.963784</td>\n",
+       "      <td>0.290985</td>\n",
+       "      <td>0.223613</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-107.440142</td>\n",
+       "      <td>36.628412</td>\n",
+       "      <td>2.015408</td>\n",
+       "      <td>1.779982</td>\n",
+       "      <td>0.070059</td>\n",
+       "      <td>1.089495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>32.511176</td>\n",
+       "      <td>42.111039</td>\n",
+       "      <td>-87.580287</td>\n",
+       "      <td>30.316254</td>\n",
+       "      <td>0.005059</td>\n",
+       "      <td>-0.016801</td>\n",
+       "      <td>1.771785</td>\n",
+       "      <td>2.923414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.706111</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-102.316795</td>\n",
+       "      <td>43.250483</td>\n",
+       "      <td>1.000075</td>\n",
+       "      <td>1.164283</td>\n",
+       "      <td>2.654329</td>\n",
+       "      <td>0.872536</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>34.121974</td>\n",
+       "      <td>38.179145</td>\n",
+       "      <td>-97.661712</td>\n",
+       "      <td>29.815675</td>\n",
+       "      <td>0.144380</td>\n",
+       "      <td>0.310666</td>\n",
+       "      <td>1.539581</td>\n",
+       "      <td>1.788519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67963</th>\n",
+       "      <td>0.056365</td>\n",
+       "      <td>11.829323</td>\n",
+       "      <td>-99.741350</td>\n",
+       "      <td>32.537026</td>\n",
+       "      <td>0.521323</td>\n",
+       "      <td>0.422672</td>\n",
+       "      <td>3.776049</td>\n",
+       "      <td>3.563149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67964</th>\n",
+       "      <td>1.926316</td>\n",
+       "      <td>8.442633</td>\n",
+       "      <td>-83.352149</td>\n",
+       "      <td>41.014260</td>\n",
+       "      <td>0.256471</td>\n",
+       "      <td>0.174457</td>\n",
+       "      <td>3.064954</td>\n",
+       "      <td>2.710515</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67965</th>\n",
+       "      <td>2.947000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-86.850348</td>\n",
+       "      <td>36.650512</td>\n",
+       "      <td>0.169112</td>\n",
+       "      <td>0.125459</td>\n",
+       "      <td>4.120613</td>\n",
+       "      <td>3.165970</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67966</th>\n",
+       "      <td>0.084062</td>\n",
+       "      <td>7.715938</td>\n",
+       "      <td>-99.906619</td>\n",
+       "      <td>31.509565</td>\n",
+       "      <td>0.495074</td>\n",
+       "      <td>0.488693</td>\n",
+       "      <td>0.945092</td>\n",
+       "      <td>1.091228</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67967</th>\n",
+       "      <td>71.365889</td>\n",
+       "      <td>69.242908</td>\n",
+       "      <td>-93.488663</td>\n",
+       "      <td>32.385969</td>\n",
+       "      <td>0.068959</td>\n",
+       "      <td>0.115068</td>\n",
+       "      <td>2.072553</td>\n",
+       "      <td>2.346426</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>67968 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       truth_tree  preds_tree         lon        lat  truth_elevation  \\\n",
+       "0        0.000000    0.000000 -119.794019  46.773157         0.423914   \n",
+       "1        0.000000    0.000000 -107.440142  36.628412         2.015408   \n",
+       "2       32.511176   42.111039  -87.580287  30.316254         0.005059   \n",
+       "3        1.706111    0.000000 -102.316795  43.250483         1.000075   \n",
+       "4       34.121974   38.179145  -97.661712  29.815675         0.144380   \n",
+       "...           ...         ...         ...        ...              ...   \n",
+       "67963    0.056365   11.829323  -99.741350  32.537026         0.521323   \n",
+       "67964    1.926316    8.442633  -83.352149  41.014260         0.256471   \n",
+       "67965    2.947000    0.000000  -86.850348  36.650512         0.169112   \n",
+       "67966    0.084062    7.715938  -99.906619  31.509565         0.495074   \n",
+       "67967   71.365889   69.242908  -93.488663  32.385969         0.068959   \n",
+       "\n",
+       "       preds_elevation  truth_population  preds_population  \n",
+       "0             0.963784          0.290985          0.223613  \n",
+       "1             1.779982          0.070059          1.089495  \n",
+       "2            -0.016801          1.771785          2.923414  \n",
+       "3             1.164283          2.654329          0.872536  \n",
+       "4             0.310666          1.539581          1.788519  \n",
+       "...                ...               ...               ...  \n",
+       "67963         0.422672          3.776049          3.563149  \n",
+       "67964         0.174457          3.064954          2.710515  \n",
+       "67965         0.125459          4.120613          3.165970  \n",
+       "67966         0.488693          0.945092          1.091228  \n",
+       "67967         0.115068          2.072553          2.346426  \n",
+       "\n",
+       "[67968 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset_folder = \"./data/\"\n",
+    "data = load_dataset(dataset_folder, \"tree_cover\")\n",
+    "data = pd.DataFrame.from_dict({item: data[item] for item in data.files})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e1a74f2",
+   "metadata": {},
+   "source": [
+    "# Example 1: Linear regression coefficient estimation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74da9199",
+   "metadata": {},
+   "source": [
+    "In this example, we estimate linear regression coefficients relating tree cover to two covariates: elevation and population. \n",
+    "\n",
+    "We treat forest cover and population variables as if we only have access to ground truth for a small subset of the points. For elevation, we will use ground truth for all points.\n",
+    "\n",
+    "In the code, truth_Y and truth_X are the full ground truth datasets for the response variable and covariates respectively. preds_Y and preds_X are the \"predicted\" datasets. Note that in this example, both truth_X and preds_X include ground truth elevation values (rather than predicted elevation values), because we are treating ground truth elevation as widely available. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d8cbab38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "truth_Y = np.array(data['truth_tree']).reshape(-1, 1)\n",
+    "preds_Y = np.array(data['preds_tree']).reshape(-1, 1)\n",
+    "\n",
+    "truth_X = np.array(data[['truth_elevation', 'truth_population']])\n",
+    "preds_X = np.array(data[['truth_elevation', 'preds_population']])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97f1df2d",
+   "metadata": {},
+   "source": [
+    "We randomly sample 500 points (out of the 67968 total points) to use as the calibration set. The remaining 67468 points are the unlabeled set.\n",
+    "\n",
+    "In the code, X and Y are the true covariate and response variable values in the calibration set. Xhat and Yhat are the the predicted covariate and response variable values in the calibration set. Xhat_unlabeled and Yhat_unlabeled are the predicted covariate and response variable values in the unlabeled set. \n",
+    "\n",
+    "Note that we add a constant column to the covariate data arrays, so that the regression coefficients will include an intercept term."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7a6b280e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(seed=100)\n",
+    "calibration_indices = np.random.choice(np.arange(0, len(data)), size=500, replace=False)\n",
+    "X = statsmodels.tools.add_constant(truth_X[calibration_indices])\n",
+    "Xhat = statsmodels.tools.add_constant(preds_X[calibration_indices])\n",
+    "Xhat_unlabeled = statsmodels.tools.add_constant(np.delete(preds_X, calibration_indices, axis=0)) # all predicted datapoints except calibration indices\n",
+    "Y = truth_Y[calibration_indices]\n",
+    "Yhat = preds_Y[calibration_indices]\n",
+    "Yhat_unlabeled = np.delete(preds_Y, calibration_indices, axis=0) # all predicted datapoints except calibration indices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67602c3c",
+   "metadata": {},
+   "source": [
+    "We use the following functions to compute point estimates and confidence intervals for the linear regression coefficients. \n",
+    "\n",
+    "We use **algorithm_linear_regression** to compute the \"true coefficient\" using the full ground truth covariate and response datasets.\n",
+    "\n",
+    "We use **ptd_linear_regression** to compute \"PTD\" point estimates and 95% confidence intervals using the calibration dataset and unlabeled dataset.\n",
+    "\n",
+    "We use **classical_linear_regression_ci** to compute \"classical\" 95% confidence intervals using only the ground truth covariate and response values in the calibration set. The corresponding point estimates are computed by averaging the lower and upper confidence interval bounds. \n",
+    "\n",
+    "We also use **classical_linear_regression_ci** to compute \"naive\" 95% confidence intervals using only the full predicted covariate and response datasets. The corresponding point estimates are computed by averaging the lower and upper confidence interval bounds. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7bc8ee68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "true_coeff = ptd.algorithm_linear_regression(data=[statsmodels.tools.add_constant(truth_X), truth_Y], w=None)\n",
+    "\n",
+    "tuning_matrix, ptd_pointestimate, ptd_ci = ptd.ptd_linear_regression(X, Xhat, Xhat_unlabeled, Y, Yhat, Yhat_unlabeled, \n",
+    "                                                                  B=2000, alpha=0.05, tuning_method='optimal')\n",
+    "\n",
+    "classical_ci = classical_linear_regression_ci(X, Y, alpha=0.05)\n",
+    "classical_pointestimate = (classical_ci[0]+classical_ci[1])/2\n",
+    "\n",
+    "naive_ci = classical_linear_regression_ci(statsmodels.tools.add_constant(preds_X), preds_Y, alpha=0.05)\n",
+    "naive_pointestimate = (naive_ci[0]+naive_ci[1])/2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09d74910",
+   "metadata": {},
+   "source": [
+    "We visualize the coefficient point estimates and confidence intervals for PTD, classical, and naive estimators. \n",
+    "\n",
+    "The yellow dotted line is the \"true coefficient.\" The effective sample size improvement of PTD compared to the classical confidence interval is shown in blue. \n",
+    "\n",
+    "We see that the naive estimates are biased relative to the true coefficient, while PTD has statistically valid confidence intervals (which, in the figure, contain the true coefficient). The PTD confidence intervals are also narrower than the classical confidence intervals.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0e0cecae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 3200x650 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams[\"font.sans-serif\"] = \"Arial\"\n",
+    "\n",
+    "plt.rc('font', size=43)          \n",
+    "plt.rc('axes', titlesize=43)     \n",
+    "plt.rc('axes', labelsize=43)    \n",
+    "plt.rc('xtick', labelsize=43)    \n",
+    "plt.rc('ytick', labelsize=43)    \n",
+    "plt.rc('figure', titlesize=43)\n",
+    "\n",
+    "covariates = ['Intercept', 'Elevation', 'Population']\n",
+    "pd.set_option(\"display.precision\", 8)\n",
+    "\n",
+    "fig = plt.figure(figsize=(32, 6.5))\n",
+    "colors = ['blue', 'green', 'red']\n",
+    "for i, covariate in enumerate(covariates):\n",
+    "        \n",
+    "    data_dict = {}\n",
+    "    classical_ci_width = classical_ci[1][i] - classical_ci[0][i]\n",
+    "    ptd_ci_width = ptd_ci[1][i] - ptd_ci[0][i]\n",
+    "    data_dict['category'] = ['PTD','Classical','Naive']\n",
+    "    data_dict['lower'] = [ptd_ci[0][i], classical_ci[0][i], naive_ci[0][i]]\n",
+    "    data_dict['upper'] = [ptd_ci[1][i], classical_ci[1][i], naive_ci[1][i]]\n",
+    "    data_dict['pointestimate'] = [ptd_pointestimate[i], classical_pointestimate[i], naive_pointestimate[i]]\n",
+    "    dataset = pd.DataFrame(data_dict)\n",
+    "    \n",
+    "    subplot = fig.add_subplot(141+i)\n",
+    "    subplot.spines['top'].set_visible(False)\n",
+    "    subplot.spines['right'].set_visible(False)\n",
+    "    subplot.spines['left'].set_visible(False)\n",
+    "    subplot.spines['bottom'].set_linewidth(2)\n",
+    "    \n",
+    "    # show true coefficient (yellow dotted line)\n",
+    "    plt.axvline(true_coeff[i], color='tab:olive', linestyle='--', lw=4, label='True \\ncoefficient')\n",
+    "    \n",
+    "    # plot confidence intervals\n",
+    "    for lower,upper,pointestimate,y in zip(dataset['lower'],dataset['upper'],dataset['pointestimate'],range(len(dataset))):\n",
+    "        subplot.scatter([pointestimate], [y], color=colors[y], s=400)\n",
+    "        subplot.scatter([lower, upper], [y, y], color=colors[y], marker='|', s=400, lw=5)\n",
+    "        subplot.plot((lower,upper),(y,y),color=colors[y], lw=6)\n",
+    "        if y == 0:\n",
+    "            # compute PTD effective sample size improvement over classical method\n",
+    "            ptd_effective_n_improvement = np.round((classical_ci_width/ptd_ci_width)**2, 1)\n",
+    "            subplot.text((lower+upper)/2, 0.2, f'{ptd_effective_n_improvement}x', fontsize = 40, c='blue', weight='bold')\n",
+    "            \n",
+    "    subplot.tick_params(axis=u'both', which=u'both',length=0)\n",
+    "    if i == 0:\n",
+    "        subplot.set_yticks(range(len(dataset)),list(dataset['category']))\n",
+    "    else:\n",
+    "        subplot.set_yticks([])\n",
+    "    subplot.set_xlabel(f'{covariate}')\n",
+    "    subplot.set_ylim(-0.2)\n",
+    "    \n",
+    "plt.tight_layout()   \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c63b651",
+   "metadata": {},
+   "source": [
+    "# Example 2: Logistic regression coefficient estimation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7834c2be",
+   "metadata": {},
+   "source": [
+    "In this example, we estimate logistic regression coefficients relating tree cover to two covariates: elevation and population. \n",
+    "\n",
+    "This example is very similar to the previous example, except we use a binarized version of the tree cover response variable for the logistic regression. Tree cover values greater than 10% are mapped to 1, while tree cover values less than or equal to 10% are mapped to 0. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6555b9b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "truth_Y = np.array(data['truth_tree'] > 10, dtype=float).reshape(-1, 1)\n",
+    "preds_Y = np.array(data['preds_tree'] > 10, dtype=float).reshape(-1, 1)\n",
+    "\n",
+    "truth_X = np.array(data[['truth_elevation', 'truth_population']])\n",
+    "preds_X = np.array(data[['truth_elevation', 'preds_population']])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9424080",
+   "metadata": {},
+   "source": [
+    "We randomly sample 1000 points (out of the 67968 total points) to use as the calibration set. The remaining 66968 points are the unlabeled set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "50740896",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(seed=100)\n",
+    "calibration_indices = np.random.choice(np.arange(0, len(data)), size=1000, replace=False)\n",
+    "X = statsmodels.tools.add_constant(truth_X[calibration_indices])\n",
+    "Xhat = statsmodels.tools.add_constant(preds_X[calibration_indices])\n",
+    "Xhat_unlabeled = statsmodels.tools.add_constant(np.delete(preds_X, calibration_indices, axis=0)) # all predicted datapoints except calibration indices\n",
+    "Y = truth_Y[calibration_indices]\n",
+    "Yhat = preds_Y[calibration_indices]\n",
+    "Yhat_unlabeled = np.delete(preds_Y, calibration_indices, axis=0) # all predicted datapoints except calibration indices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58cdcdc9",
+   "metadata": {},
+   "source": [
+    "We use the following functions to compute point estimates and confidence intervals for the logistic regression coefficients.\n",
+    "\n",
+    "We use **algorithm_logistic_regression** to compute the \"true coefficient\" using the full ground truth covariate and response datasets.\n",
+    "\n",
+    "We use **ptd_logistic_regression** to compute \"PTD\" point estimates and 95% confidence intervals using the calibration dataset and unlabeled dataset.\n",
+    "\n",
+    "We use **classical_logistic_regression_ci** to compute \"classical\" 95% confidence intervals using only the ground truth covariate and response values in the calibration set. The corresponding point estimates are computed by averaging the lower and upper confidence interval bounds. \n",
+    "\n",
+    "We also use **classical_logistic_regression_ci** to compute \"naive\" 95% confidence intervals using only the full predicted covariate and response datasets. The corresponding point estimates are computed by averaging the lower and upper confidence interval bounds. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "33ad89da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "true_coeff = ptd.algorithm_logistic_regression(data=[statsmodels.tools.add_constant(truth_X), truth_Y], w=None)\n",
+    "\n",
+    "tuning_matrix, ptd_pointestimate, ptd_ci = ptd.ptd_logistic_regression(X, Xhat, Xhat_unlabeled, Y, Yhat, Yhat_unlabeled, \n",
+    "                                                                  B=2000, alpha=0.05, tuning_method='optimal')\n",
+    "\n",
+    "classical_ci = classical_logistic_regression_ci(X, Y, alpha=0.05)\n",
+    "classical_pointestimate = (classical_ci[0]+classical_ci[1])/2\n",
+    "\n",
+    "naive_ci = classical_logistic_regression_ci(statsmodels.tools.add_constant(preds_X), preds_Y, alpha=0.05)\n",
+    "naive_pointestimate = (naive_ci[0]+naive_ci[1])/2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d0079f7",
+   "metadata": {},
+   "source": [
+    "We visualize the coefficient point estimates and confidence intervals for PTD, classical, and naive estimators.\n",
+    "\n",
+    "The yellow dotted line is the \"true coefficient.\" The effective sample size improvement of PTD compared to the classical confidence interval is shown in blue.\n",
+    "\n",
+    "We see that the naive estimates are biased relative to the true coefficient, while PTD has statistically valid confidence intervals (which, in the figure, contain the true coefficient). The PTD confidence intervals for intercept and elevation are also narrower than the classical confidence intervals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e6b7408f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 3200x650 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams[\"font.sans-serif\"] = \"Arial\"\n",
+    "\n",
+    "plt.rc('font', size=43)          \n",
+    "plt.rc('axes', titlesize=43)     \n",
+    "plt.rc('axes', labelsize=43)    \n",
+    "plt.rc('xtick', labelsize=43)    \n",
+    "plt.rc('ytick', labelsize=43)    \n",
+    "plt.rc('figure', titlesize=43)\n",
+    "\n",
+    "covariates = ['Intercept', 'Elevation', 'Population']\n",
+    "pd.set_option(\"display.precision\", 8)\n",
+    "\n",
+    "fig = plt.figure(figsize=(32, 6.5))\n",
+    "colors = ['blue', 'green', 'red']\n",
+    "for i, covariate in enumerate(covariates):\n",
+    "        \n",
+    "    data_dict = {}\n",
+    "    classical_ci_width = classical_ci[1][i] - classical_ci[0][i]\n",
+    "    ptd_ci_width = ptd_ci[1][i] - ptd_ci[0][i]\n",
+    "    data_dict['category'] = ['PTD','Classical','Naive']\n",
+    "    data_dict['lower'] = [ptd_ci[0][i], classical_ci[0][i], naive_ci[0][i]]\n",
+    "    data_dict['upper'] = [ptd_ci[1][i], classical_ci[1][i], naive_ci[1][i]]\n",
+    "    data_dict['pointestimate'] = [ptd_pointestimate[i], classical_pointestimate[i], naive_pointestimate[i]]\n",
+    "    dataset = pd.DataFrame(data_dict)\n",
+    "    \n",
+    "    subplot = fig.add_subplot(141+i)\n",
+    "    subplot.spines['top'].set_visible(False)\n",
+    "    subplot.spines['right'].set_visible(False)\n",
+    "    subplot.spines['left'].set_visible(False)\n",
+    "    subplot.spines['bottom'].set_linewidth(2)\n",
+    "    \n",
+    "    # show true coefficient (yellow dotted line)\n",
+    "    plt.axvline(true_coeff[i], color='tab:olive', linestyle='--', lw=4, label='True \\ncoefficient')\n",
+    "    \n",
+    "    # plot confidence intervals\n",
+    "    for lower,upper,pointestimate,y in zip(dataset['lower'],dataset['upper'],dataset['pointestimate'],range(len(dataset))):\n",
+    "        subplot.scatter([pointestimate], [y], color=colors[y], s=400)\n",
+    "        subplot.scatter([lower, upper], [y, y], color=colors[y], marker='|', s=400, lw=5)\n",
+    "        subplot.plot((lower,upper),(y,y),color=colors[y], lw=6)\n",
+    "        if y == 0:\n",
+    "            # compute PTD effective sample size improvement over classical method\n",
+    "            ptd_effective_n_improvement = np.round((classical_ci_width/ptd_ci_width)**2, 1)\n",
+    "            subplot.text((lower+upper)/2, 0.2, f'{ptd_effective_n_improvement}x', fontsize = 40, c='blue', weight='bold')\n",
+    "            \n",
+    "    subplot.tick_params(axis=u'both', which=u'both',length=0)\n",
+    "    if i == 0:\n",
+    "        subplot.set_yticks(range(len(dataset)),list(dataset['category']))\n",
+    "    else:\n",
+    "        subplot.set_yticks([])\n",
+    "    subplot.set_xlabel(f'{covariate}')\n",
+    "    subplot.set_ylim(-0.2)\n",
+    "    \n",
+    "plt.tight_layout()   \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "301723d1",
+   "metadata": {},
+   "source": [
+    "# Example 3: Using Predict-Then-Debias for an estimator of interest (quantile estimation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9f1afe0",
+   "metadata": {},
+   "source": [
+    "Users can also use the ptd module to apply the PTD method on their own estimators of interest. \n",
+    "\n",
+    "As an example, we will show how to create a quantile estimation wrapper function around the ptd module's **ptd_bootstrap** function. We will then use the wrapper function to estimate the 0.8 quantile (80th percentile) value for tree cover.\n",
+    "\n",
+    "The **ptd_bootstrap** function has the following inputs and outputs.\n",
+    "\n",
+    "    Inputs:\n",
+    "        algorithm (function): python function that takes in data and weights, and returns array containing parameters of interest (e.g., a function that computes linear regression or logistic regression coefficients)\n",
+    "        data_truth (List[ndarray]): ground truth labeled data (each ndarray has n rows)\n",
+    "        data_pred (List[ndarray]): predicted labeled data (each ndarray has n rows)\n",
+    "        data_pred_unlabeled (List[ndarray]): predicted unlabeled data (each ndarray has N rows)\n",
+    "        w (ndarray, optional): sample weights for labeled data (length n)\n",
+    "        w_unlabeled (ndarray, optional): sample weights for unlabeled data (length N)\n",
+    "        B (int, optional): number of bootstrap steps\n",
+    "        alpha (float, optional): error level (must be in the range (0, 1)). The PTD confidence interval will target a coverage of 1 - alpha. \n",
+    "        tuning_method (str, optional): method used to create the tuning matrix: \"optimal_diagonal\", \"optimal\", or None. (If tuning_method is None, the identity matrix is used.) \n",
+    "        \n",
+    "    Outputs:\n",
+    "        ndarray: the tuning matrix (dimensions d x d) computed from the selected tuning method\n",
+    "        ndarray: PTD point estimate of the parameters of interest (length d)\n",
+    "        tuple: lower and upper bounds of PTD confidence intervals with (1-alpha) coverage\n",
+    "\n",
+    "Below we define the **ptd_quantile** wrapper function for quantile estimation. Within **ptd_quantile**, we first define the **algorithm_quantile** function, which takes in a dataset and computes the selected empirical quantile. We then pass the **algorithm_quantile** function (along with the datasets and hyperparameters) into the **ptd_bootstrap** function, which will use the PTD method to compute the quantile point estimate and confidence interval. \n",
+    "\n",
+    "(Note that the first input to the **ptd_bootstrap** is required to be a python function that takes in two inputs (data and weights). So we define **algorithm_quantile** to have both data and weights as arguments, even though the weights are not used.) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "0a9a94bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ptd_quantile(X, Xhat, Xhat_unlabeled, quantile, B=2000, alpha=0.05, tuning_method='optimal_diagonal'):\n",
+    "    \"\"\"\n",
+    "    Computes tuning matrix, point estimates, and confidence intervals for quantile estimation using the Predict-then-Debias bootstrap algorithm. \n",
+    "    \n",
+    "    Args:\n",
+    "        X (ndarray): ground truth values in labeled data (dimensions n x 1)\n",
+    "        Xhat (ndarray): predicted values in labeled data (dimensions n x 1)\n",
+    "        Xhat_unlabeled (ndarray): predicted values in unlabeled data (dimensions N x 1)\n",
+    "        quantile (scalar): the desired quantile probability. Must be in the range [0, 1].\n",
+    "        B (int, optional): number of bootstrap steps\n",
+    "        alpha (float, optional): error level (must be in the range (0, 1)). The PTD confidence interval will target a coverage of 1 - alpha. \n",
+    "        tuning_method (str, optional): method used to create the tuning matrix: \"optimal_diagonal\", \"optimal\", or None. (If tuning_method is None, the identity matrix is used.) \n",
+    "        \n",
+    "    Returns:\n",
+    "        ndarray: the tuning matrix computed from the selected tuning method (1 x 1)\n",
+    "        ndarray: PTD point estimate of the quantile \n",
+    "        tuple: lower and upper bounds of PTD confidence intervals with (1-alpha) coverage\n",
+    "    \"\"\"\n",
+    "    def algorithm_quantile(data, weights):\n",
+    "        pointestimate = np.quantile(data, quantile)\n",
+    "        return np.array([pointestimate])\n",
+    "    \n",
+    "    # ptd_bootstrap requires input data arrays to be in list form (List[ndarray])\n",
+    "    data_truth = [X]\n",
+    "    data_pred = [Xhat]\n",
+    "    data_pred_unlabeled = [Xhat_unlabeled]\n",
+    "    \n",
+    "    return ptd.ptd_bootstrap(algorithm_quantile, data_truth, data_pred, data_pred_unlabeled, B=B, alpha=alpha, tuning_method=tuning_method)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55d6102f",
+   "metadata": {},
+   "source": [
+    "We also define a function **classical_quantile_ci** which computes a confidence interval for a given quantile of a single dataset X. This will be used to compute \"classical\" (ground truth only) and \"naive\" (prediction only) quantile confidence intervals. \n",
+    "\n",
+    "For more information see https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mstats.mquantiles_cimj.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "085d49e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats.mstats import mquantiles_cimj\n",
+    "\n",
+    "def classical_quantile_ci(X, quantile, alpha=0.05):\n",
+    "    ci = mquantiles_cimj(X, prob=quantile, alpha=alpha)\n",
+    "    return ci"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71e0e6ad",
+   "metadata": {},
+   "source": [
+    "We define truth_X as the full ground truth tree cover dataset, and preds_X as the full predicted tree cover dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "298e7e3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "truth_X = np.array(data['truth_tree']).reshape(-1, 1)\n",
+    "preds_X = np.array(data['preds_tree']).reshape(-1, 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e982d64",
+   "metadata": {},
+   "source": [
+    "We randomly sample 500 points (out of the 67968 total points) to use as the calibration set. The remaining 67468 points are the unlabeled set.\n",
+    "\n",
+    "In the code below, X contains the ground truth values in the calibration set, Xhat contains the predicted values in the calibration set, and Xhat_unlabeled contains the predicted values in the unlabeled set. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a3805c2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(seed=100)\n",
+    "calibration_indices = np.random.choice(np.arange(0, len(data)), size=500, replace=False)\n",
+    "X = truth_X[calibration_indices]\n",
+    "Xhat = preds_X[calibration_indices]\n",
+    "Xhat_unlabeled = np.delete(preds_X, calibration_indices, axis=0) # all predicted datapoints except calibration indices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0698af08",
+   "metadata": {},
+   "source": [
+    "We compute point estimates and 95% confidence intervals for the 0.8 quantile value for tree cover. \n",
+    "\n",
+    "We use **np.quantile** to compute the true 0.8 quantile for tree cover using the full ground truth dataset. \n",
+    "\n",
+    "We use **ptd_quantile** to compute the \"PTD\" point estimate and 95% confidence interval using the calibration dataset and unlabeled dataset.\n",
+    "\n",
+    "We use **classical_quantile_ci** to compute the \"classical\" 95% confidence interval using only the ground truth values in the calibration set. The corresponding point estimates are computed by averaging the lower and upper confidence interval bounds. \n",
+    "\n",
+    "We also use **classical_quantile_ci** to compute the \"naive\" 95% confidence interval using only the full prediction dataset. The corresponding point estimates are computed by averaging the lower and upper confidence interval bounds. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "8bdb893b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "quantile = 0.8\n",
+    "true_coeff = [np.quantile(truth_X, quantile)]\n",
+    "\n",
+    "tuning_matrix, ptd_pointestimate, ptd_ci = ptd_quantile(X, Xhat, Xhat_unlabeled, quantile, \n",
+    "                                                                  B=2000, alpha=0.05, tuning_method='optimal')\n",
+    "\n",
+    "classical_ci = classical_quantile_ci(X, quantile, alpha=0.05)\n",
+    "classical_pointestimate = (classical_ci[0]+classical_ci[1])/2\n",
+    "\n",
+    "naive_ci = classical_quantile_ci(preds_X, quantile, alpha=0.05)\n",
+    "naive_pointestimate = (naive_ci[0]+naive_ci[1])/2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f9b9c50",
+   "metadata": {},
+   "source": [
+    "We visualize the quantile point estimate and confidence interval for PTD, classical, and naive estimators.\n",
+    "\n",
+    "The yellow dotted line is the \"true 0.8 quantile value\" for tree cover. The effective sample size improvement of PTD compared to the classical confidence interval is shown in blue.\n",
+    "\n",
+    "We see that the naive estimate is biased relative to the true quantile value, while PTD has a statistically valid confidence interval (which, in the figure, contains the true quantile value). The PTD confidence interval is also narrower than the classical confidence interval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "42c6eed4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 3500x650 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams[\"font.sans-serif\"] = \"Arial\"\n",
+    "\n",
+    "plt.rc('font', size=43)          \n",
+    "plt.rc('axes', titlesize=43)     \n",
+    "plt.rc('axes', labelsize=43)    \n",
+    "plt.rc('xtick', labelsize=43)    \n",
+    "plt.rc('ytick', labelsize=43)    \n",
+    "plt.rc('figure', titlesize=43)\n",
+    "\n",
+    "variables = [f'Tree Cover {100*quantile}% quantile']\n",
+    "pd.set_option(\"display.precision\", 8)\n",
+    "\n",
+    "fig = plt.figure(figsize=(35, 6.5))\n",
+    "colors = ['blue', 'green', 'red']\n",
+    "for i, variable in enumerate(variables):\n",
+    "        \n",
+    "    data_dict = {}\n",
+    "    classical_ci_width = classical_ci[1][i] - classical_ci[0][i]\n",
+    "    ptd_ci_width = ptd_ci[1][i] - ptd_ci[0][i]\n",
+    "    data_dict['category'] = ['PTD','Classical','Naive']\n",
+    "    data_dict['lower'] = [ptd_ci[0][i], classical_ci[0][i], naive_ci[0][i]]\n",
+    "    data_dict['upper'] = [ptd_ci[1][i], classical_ci[1][i], naive_ci[1][i]]\n",
+    "    data_dict['pointestimate'] = [ptd_pointestimate[i], classical_pointestimate[i], naive_pointestimate[i]]\n",
+    "    dataset = pd.DataFrame(data_dict)\n",
+    "    \n",
+    "    subplot = fig.add_subplot(141+i)\n",
+    "    subplot.spines['top'].set_visible(False)\n",
+    "    subplot.spines['right'].set_visible(False)\n",
+    "    subplot.spines['left'].set_visible(False)\n",
+    "    subplot.spines['bottom'].set_linewidth(2)\n",
+    "    \n",
+    "    # show true coefficient (yellow dotted line)\n",
+    "    plt.axvline(true_coeff[i], color='tab:olive', linestyle='--', lw=4, label='True \\ncoefficient')\n",
+    "    \n",
+    "    # plot confidence intervals\n",
+    "    for lower,upper,pointestimate,y in zip(dataset['lower'],dataset['upper'],dataset['pointestimate'],range(len(dataset))):\n",
+    "        subplot.scatter([pointestimate], [y], color=colors[y], s=400)\n",
+    "        subplot.scatter([lower, upper], [y, y], color=colors[y], marker='|', s=400, lw=5)\n",
+    "        subplot.plot((lower,upper),(y,y),color=colors[y], lw=6)\n",
+    "        if y == 0:\n",
+    "            # compute PTD effective sample size improvement over classical method\n",
+    "            ptd_effective_n_improvement = np.round((classical_ci_width/ptd_ci_width)**2, 1)\n",
+    "            subplot.text((lower+upper)/2, 0.2, f'{ptd_effective_n_improvement}x', fontsize = 40, c='blue', weight='bold')\n",
+    "            \n",
+    "    subplot.tick_params(axis=u'both', which=u'both',length=0)\n",
+    "    if i == 0:\n",
+    "        subplot.set_yticks(range(len(dataset)),list(dataset['category']))\n",
+    "    else:\n",
+    "        subplot.set_yticks([])\n",
+    "    subplot.set_xlabel(f'{variable}')\n",
+    "    subplot.set_ylim(-0.2)\n",
+    "    \n",
+    "plt.tight_layout()   \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c05961b",
+   "metadata": {},
+   "source": [
+    "# Example 4: Weighted linear regression coefficient estimation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2299539e",
+   "metadata": {},
+   "source": [
+    "In this example, we show how to estimate linear regression coefficients when the calibration dataset is obtained by a weighted sampling scheme, such that some points are more likely to be sampled than others. \n",
+    "\n",
+    "We use the same setup as Example 1. The goal is to estimate linear regression coefficients relating tree cover to two covariates (elevation and population). The calibration set will be chosen using weighted sampling based on longitude, such that points in the east are more likely to be sampled than points in the west.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "9273049b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "truth_Y = np.array(data['truth_tree']).reshape(-1, 1)\n",
+    "preds_Y = np.array(data['preds_tree']).reshape(-1, 1)\n",
+    "\n",
+    "truth_X = np.array(data[['truth_elevation', 'truth_population']])\n",
+    "preds_X = np.array(data[['truth_elevation', 'preds_population']])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3daf1122",
+   "metadata": {},
+   "source": [
+    "For the weighted sampling, we first partition the dataset into 4 quartiles (i=1, 2, 3, 4) based on longitude, such that the westernmost quartile is i=1 and the easternmost quartile is i=4.\n",
+    "\n",
+    "Each point in quartile i is given the score $$q(i)=5^i$$\n",
+    "\n",
+    "Finally, we assign a sampling probability to each point by normalizing the scores to sum to 1. By design, points in the east will have a higher probability of being included in the calibration set than points in the west. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "7e5c5a75",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\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>truth_tree</th>\n",
+       "      <th>preds_tree</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>truth_elevation</th>\n",
+       "      <th>preds_elevation</th>\n",
+       "      <th>truth_population</th>\n",
+       "      <th>preds_population</th>\n",
+       "      <th>quartile_number</th>\n",
+       "      <th>quartile_score</th>\n",
+       "      <th>probability</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.00000000</td>\n",
+       "      <td>0.00000000</td>\n",
+       "      <td>-119.79401861</td>\n",
+       "      <td>46.77315694</td>\n",
+       "      <td>0.42391388</td>\n",
+       "      <td>0.96378381</td>\n",
+       "      <td>0.29098461</td>\n",
+       "      <td>0.22361307</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.00000038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.00000000</td>\n",
+       "      <td>0.00000000</td>\n",
+       "      <td>-107.44014215</td>\n",
+       "      <td>36.62841178</td>\n",
+       "      <td>2.01540807</td>\n",
+       "      <td>1.77998188</td>\n",
+       "      <td>0.07005862</td>\n",
+       "      <td>1.08949520</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.00000038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>32.51117590</td>\n",
+       "      <td>42.11103850</td>\n",
+       "      <td>-87.58028724</td>\n",
+       "      <td>30.31625421</td>\n",
+       "      <td>0.00505927</td>\n",
+       "      <td>-0.01680112</td>\n",
+       "      <td>1.77178479</td>\n",
+       "      <td>2.92341369</td>\n",
+       "      <td>3</td>\n",
+       "      <td>125</td>\n",
+       "      <td>0.00000943</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.70611111</td>\n",
+       "      <td>0.00000000</td>\n",
+       "      <td>-102.31679539</td>\n",
+       "      <td>43.25048344</td>\n",
+       "      <td>1.00007475</td>\n",
+       "      <td>1.16428282</td>\n",
+       "      <td>2.65432932</td>\n",
+       "      <td>0.87253554</td>\n",
+       "      <td>2</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.00000189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>34.12197437</td>\n",
+       "      <td>38.17914530</td>\n",
+       "      <td>-97.66171151</td>\n",
+       "      <td>29.81567453</td>\n",
+       "      <td>0.14438043</td>\n",
+       "      <td>0.31066607</td>\n",
+       "      <td>1.53958068</td>\n",
+       "      <td>1.78851923</td>\n",
+       "      <td>2</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.00000189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67963</th>\n",
+       "      <td>0.05636540</td>\n",
+       "      <td>11.82932282</td>\n",
+       "      <td>-99.74134957</td>\n",
+       "      <td>32.53702611</td>\n",
+       "      <td>0.52132315</td>\n",
+       "      <td>0.42267153</td>\n",
+       "      <td>3.77604851</td>\n",
+       "      <td>3.56314923</td>\n",
+       "      <td>2</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.00000189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67964</th>\n",
+       "      <td>1.92631579</td>\n",
+       "      <td>8.44263317</td>\n",
+       "      <td>-83.35214891</td>\n",
+       "      <td>41.01426028</td>\n",
+       "      <td>0.25647059</td>\n",
+       "      <td>0.17445653</td>\n",
+       "      <td>3.06495415</td>\n",
+       "      <td>2.71051530</td>\n",
+       "      <td>4</td>\n",
+       "      <td>625</td>\n",
+       "      <td>0.00004714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67965</th>\n",
+       "      <td>2.94700000</td>\n",
+       "      <td>0.00000000</td>\n",
+       "      <td>-86.85034805</td>\n",
+       "      <td>36.65051239</td>\n",
+       "      <td>0.16911172</td>\n",
+       "      <td>0.12545899</td>\n",
+       "      <td>4.12061274</td>\n",
+       "      <td>3.16597032</td>\n",
+       "      <td>3</td>\n",
+       "      <td>125</td>\n",
+       "      <td>0.00000943</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67966</th>\n",
+       "      <td>0.08406220</td>\n",
+       "      <td>7.71593830</td>\n",
+       "      <td>-99.90661882</td>\n",
+       "      <td>31.50956468</td>\n",
+       "      <td>0.49507440</td>\n",
+       "      <td>0.48869264</td>\n",
+       "      <td>0.94509188</td>\n",
+       "      <td>1.09122816</td>\n",
+       "      <td>2</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.00000189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67967</th>\n",
+       "      <td>71.36588921</td>\n",
+       "      <td>69.24290786</td>\n",
+       "      <td>-93.48866294</td>\n",
+       "      <td>32.38596910</td>\n",
+       "      <td>0.06895927</td>\n",
+       "      <td>0.11506807</td>\n",
+       "      <td>2.07255260</td>\n",
+       "      <td>2.34642563</td>\n",
+       "      <td>3</td>\n",
+       "      <td>125</td>\n",
+       "      <td>0.00000943</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>67968 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        truth_tree   preds_tree           lon          lat  truth_elevation  \\\n",
+       "0       0.00000000   0.00000000 -119.79401861  46.77315694       0.42391388   \n",
+       "1       0.00000000   0.00000000 -107.44014215  36.62841178       2.01540807   \n",
+       "2      32.51117590  42.11103850  -87.58028724  30.31625421       0.00505927   \n",
+       "3       1.70611111   0.00000000 -102.31679539  43.25048344       1.00007475   \n",
+       "4      34.12197437  38.17914530  -97.66171151  29.81567453       0.14438043   \n",
+       "...            ...          ...           ...          ...              ...   \n",
+       "67963   0.05636540  11.82932282  -99.74134957  32.53702611       0.52132315   \n",
+       "67964   1.92631579   8.44263317  -83.35214891  41.01426028       0.25647059   \n",
+       "67965   2.94700000   0.00000000  -86.85034805  36.65051239       0.16911172   \n",
+       "67966   0.08406220   7.71593830  -99.90661882  31.50956468       0.49507440   \n",
+       "67967  71.36588921  69.24290786  -93.48866294  32.38596910       0.06895927   \n",
+       "\n",
+       "       preds_elevation  truth_population  preds_population  quartile_number  \\\n",
+       "0           0.96378381        0.29098461        0.22361307                1   \n",
+       "1           1.77998188        0.07005862        1.08949520                1   \n",
+       "2          -0.01680112        1.77178479        2.92341369                3   \n",
+       "3           1.16428282        2.65432932        0.87253554                2   \n",
+       "4           0.31066607        1.53958068        1.78851923                2   \n",
+       "...                ...               ...               ...              ...   \n",
+       "67963       0.42267153        3.77604851        3.56314923                2   \n",
+       "67964       0.17445653        3.06495415        2.71051530                4   \n",
+       "67965       0.12545899        4.12061274        3.16597032                3   \n",
+       "67966       0.48869264        0.94509188        1.09122816                2   \n",
+       "67967       0.11506807        2.07255260        2.34642563                3   \n",
+       "\n",
+       "       quartile_score  probability  \n",
+       "0                   5   0.00000038  \n",
+       "1                   5   0.00000038  \n",
+       "2                 125   0.00000943  \n",
+       "3                  25   0.00000189  \n",
+       "4                  25   0.00000189  \n",
+       "...               ...          ...  \n",
+       "67963              25   0.00000189  \n",
+       "67964             625   0.00004714  \n",
+       "67965             125   0.00000943  \n",
+       "67966              25   0.00000189  \n",
+       "67967             125   0.00000943  \n",
+       "\n",
+       "[67968 rows x 11 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(seed=100)\n",
+    "# map each point to its longitude quartile (1, 2, 3, or 4)\n",
+    "perc = np.percentile(data['lon'], [25,50,75])\n",
+    "data['quartile_number'] = 1 + np.digitize(data['lon'], perc)\n",
+    "\n",
+    "# assign quartile scores q(i) = 5^i\n",
+    "data['quartile_score'] = np.array([5**i for i in data['quartile_number']])\n",
+    "\n",
+    "# compute probability of sampling each point by normalizing the quartile scores to sum to 1 \n",
+    "data['probability'] = data['quartile_score']/data['quartile_score'].sum()\n",
+    "\n",
+    "display(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0827fc9",
+   "metadata": {},
+   "source": [
+    "Using these sampling probabilities, we randomly sample n=1000 points (out of the 67968 total points) to use as the calibration set. The remaining 66968 points are the unlabeled set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "484cbe05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sample calibration set using the given probabilities\n",
+    "n = 1000\n",
+    "calibration_indices = np.random.choice(np.arange(0, len(data)), size=n, replace=False, p=data['probability'])\n",
+    "\n",
+    "X = statsmodels.tools.add_constant(truth_X[calibration_indices])\n",
+    "Xhat = statsmodels.tools.add_constant(preds_X[calibration_indices])\n",
+    "Xhat_unlabeled = statsmodels.tools.add_constant(np.delete(preds_X, calibration_indices, axis=0)) # all predicted datapoints except calibration indices\n",
+    "Y = truth_Y[calibration_indices]\n",
+    "Yhat = preds_Y[calibration_indices]\n",
+    "Yhat_unlabeled = np.delete(preds_Y, calibration_indices, axis=0) # all predicted datapoints except calibration indices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a934bc61",
+   "metadata": {},
+   "source": [
+    "In order to get approximately unbiased estimates of the linear regression coefficients, we will need to compute sample weights for the calibration set and the unlabeled set. \n",
+    "\n",
+    "We use Inverse Probability Weighting. A calibration point with sampling probability p has weight 1/p, while an unlabeled point with sampling probability p has weight 1/(1-p). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "b6fbb969",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# to compute sample weights, we need to rescale the probabilities so they sum to n (the number of calibration samples)\n",
+    "data['probability'] = n*data['probability']  \n",
+    "\n",
+    "# sample weights (Inverse Probability Weighting)\n",
+    "w = (1/data['probability'])[calibration_indices].to_numpy()\n",
+    "w_unlabeled = np.delete(1/(1-data['probability']), calibration_indices, axis=0) # all predicted datapoints except calibration indices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fba902d",
+   "metadata": {},
+   "source": [
+    "We use the same functions as Example 1 to compute point estimates and confidence intervals for the linear regression coefficients. We will compare the true coefficient with the PTD, classical, and naive estimators. \n",
+    "\n",
+    "Note that we use the sample weights w and w_unlabeled as inputs to **ptd_linear_regression**. We also use w as an input to **classical_linear_regression_ci**. (We do not use any weights for the naive estimator, since it uses the full predicted dataset and does not use the calibration set.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "86544473",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "true_coeff = ptd.algorithm_linear_regression(data=[statsmodels.tools.add_constant(truth_X), truth_Y], w=None)\n",
+    "\n",
+    "tuning_matrix, ptd_pointestimate, ptd_ci = ptd.ptd_linear_regression(X, Xhat, Xhat_unlabeled, Y, Yhat, Yhat_unlabeled, \n",
+    "                                                                             w=w, w_unlabeled=w_unlabeled,\n",
+    "                                                                  B=2000, alpha=0.05, tuning_method='optimal')\n",
+    "\n",
+    "classical_ci = classical_linear_regression_ci(X, Y, alpha=0.05, w=w)\n",
+    "classical_pointestimate = (classical_ci[0]+classical_ci[1])/2\n",
+    "\n",
+    "naive_ci = classical_linear_regression_ci(statsmodels.tools.add_constant(preds_X), preds_Y, alpha=0.05)\n",
+    "naive_pointestimate = (naive_ci[0]+naive_ci[1])/2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fae1a11",
+   "metadata": {},
+   "source": [
+    "We visualize the coefficient point estimates and confidence intervals for PTD, classical, and naive estimators.\n",
+    "\n",
+    "The yellow dotted line is the \"true coefficient.\" The effective sample size improvement of PTD compared to the classical confidence interval is shown in blue.\n",
+    "\n",
+    "We see that the naive estimates are biased relative to the true coefficient, while PTD has statistically valid confidence intervals (which, in the figure, contain the true coefficient). The PTD confidence intervals are also narrower than the classical confidence intervals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "560a5d53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>category</th>\n",
+       "      <th>lower</th>\n",
+       "      <th>upper</th>\n",
+       "      <th>pointestimate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PTD (weighted)</td>\n",
+       "      <td>28.01811321</td>\n",
+       "      <td>38.96394310</td>\n",
+       "      <td>34.74788264</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Classical (weighted)</td>\n",
+       "      <td>18.81459014</td>\n",
+       "      <td>52.99687891</td>\n",
+       "      <td>35.90573453</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Naive</td>\n",
+       "      <td>29.20621500</td>\n",
+       "      <td>30.22704453</td>\n",
+       "      <td>29.71662976</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               category        lower        upper  pointestimate\n",
+       "0        PTD (weighted)  28.01811321  38.96394310    34.74788264\n",
+       "1  Classical (weighted)  18.81459014  52.99687891    35.90573453\n",
+       "2                 Naive  29.20621500  30.22704453    29.71662976"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>category</th>\n",
+       "      <th>lower</th>\n",
+       "      <th>upper</th>\n",
+       "      <th>pointestimate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PTD (weighted)</td>\n",
+       "      <td>-17.29942190</td>\n",
+       "      <td>-7.97645045</td>\n",
+       "      <td>-13.65211767</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Classical (weighted)</td>\n",
+       "      <td>-16.23255623</td>\n",
+       "      <td>5.33752187</td>\n",
+       "      <td>-5.44751718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Naive</td>\n",
+       "      <td>-10.29206477</td>\n",
+       "      <td>-9.58677065</td>\n",
+       "      <td>-9.93941771</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               category        lower       upper  pointestimate\n",
+       "0        PTD (weighted) -17.29942190 -7.97645045   -13.65211767\n",
+       "1  Classical (weighted) -16.23255623  5.33752187    -5.44751718\n",
+       "2                 Naive -10.29206477 -9.58677065    -9.93941771"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>category</th>\n",
+       "      <th>lower</th>\n",
+       "      <th>upper</th>\n",
+       "      <th>pointestimate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PTD (weighted)</td>\n",
+       "      <td>-2.02773871</td>\n",
+       "      <td>2.83060198</td>\n",
+       "      <td>0.22872327</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Classical (weighted)</td>\n",
+       "      <td>-5.53153851</td>\n",
+       "      <td>4.61908689</td>\n",
+       "      <td>-0.45622581</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Naive</td>\n",
+       "      <td>1.87056191</td>\n",
+       "      <td>2.19331316</td>\n",
+       "      <td>2.03193753</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               category       lower       upper  pointestimate\n",
+       "0        PTD (weighted) -2.02773871  2.83060198     0.22872327\n",
+       "1  Classical (weighted) -5.53153851  4.61908689    -0.45622581\n",
+       "2                 Naive  1.87056191  2.19331316     2.03193753"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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",
+      "text/plain": [
+       "<Figure size 3200x650 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams[\"font.sans-serif\"] = \"Arial\"\n",
+    "\n",
+    "plt.rc('font', size=43)          \n",
+    "plt.rc('axes', titlesize=43)     \n",
+    "plt.rc('axes', labelsize=43)    \n",
+    "plt.rc('xtick', labelsize=43)    \n",
+    "plt.rc('ytick', labelsize=43)    \n",
+    "plt.rc('figure', titlesize=43)\n",
+    "\n",
+    "covariates = ['Intercept', 'Elevation', 'Population']\n",
+    "pd.set_option(\"display.precision\", 8)\n",
+    "\n",
+    "fig = plt.figure(figsize=(32, 6.5))\n",
+    "colors = ['blue', 'green', 'red']\n",
+    "for i, covariate in enumerate(covariates):\n",
+    "        \n",
+    "    data_dict = {}\n",
+    "    classical_ci_width = classical_ci[1][i] - classical_ci[0][i]\n",
+    "    ptd_ci_width = ptd_ci[1][i] - ptd_ci[0][i]\n",
+    "    data_dict['category'] = ['PTD (weighted)', 'Classical (weighted)','Naive']\n",
+    "    data_dict['lower'] = [ptd_ci[0][i], classical_ci[0][i], naive_ci[0][i]]\n",
+    "    data_dict['upper'] = [ptd_ci[1][i], classical_ci[1][i], naive_ci[1][i]]\n",
+    "    data_dict['pointestimate'] = [ptd_pointestimate[i], classical_pointestimate[i], naive_pointestimate[i]]\n",
+    "    dataset = pd.DataFrame(data_dict)\n",
+    "    display(dataset)\n",
+    "    \n",
+    "    subplot = fig.add_subplot(141+i)\n",
+    "    subplot.spines['top'].set_visible(False)\n",
+    "    subplot.spines['right'].set_visible(False)\n",
+    "    subplot.spines['left'].set_visible(False)\n",
+    "    subplot.spines['bottom'].set_linewidth(2)\n",
+    "    \n",
+    "    # show true coefficient (yellow dotted line)\n",
+    "    plt.axvline(true_coeff[i], color='tab:olive', linestyle='--', lw=4, label='True \\ncoefficient')\n",
+    "    \n",
+    "    # plot confidence intervals\n",
+    "    for lower,upper,pointestimate,y in zip(dataset['lower'],dataset['upper'],dataset['pointestimate'],range(len(dataset))):\n",
+    "        subplot.scatter([pointestimate], [y], color=colors[y], s=400)\n",
+    "        subplot.scatter([lower, upper], [y, y], color=colors[y], marker='|', s=400, lw=5)\n",
+    "        subplot.plot((lower,upper),(y,y),color=colors[y], lw=6)\n",
+    "        if y == 0:\n",
+    "            # compute PTD effective sample size improvement over classical method\n",
+    "            ptd_effective_n_improvement = np.round((classical_ci_width/ptd_ci_width)**2, 1)\n",
+    "            subplot.text((lower+upper)/2, 0.2, f'{ptd_effective_n_improvement}x', fontsize = 40, c='blue', weight='bold')\n",
+    "            \n",
+    "    subplot.tick_params(axis=u'both', which=u'both',length=0)\n",
+    "    if i == 0:\n",
+    "        subplot.set_yticks(range(len(dataset)),list(dataset['category']))\n",
+    "    else:\n",
+    "        subplot.set_yticks([])\n",
+    "    subplot.set_xlabel(f'{covariate}')\n",
+    "    subplot.set_ylim(-0.2)\n",
+    "    \n",
+    "plt.tight_layout()   \n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ppi_py/__init__.py b/ppi_py/__init__.py
index 3457610..47cbd20 100644
--- a/ppi_py/__init__.py
+++ b/ppi_py/__init__.py
@@ -2,3 +2,4 @@
 from .cross_ppi import *
 from .baselines import *
 from .ppi_power_analysis import *
+from .ptd import *
diff --git a/ppi_py/datasets/datasets.py b/ppi_py/datasets/datasets.py
index 3c82b4a..02e1b77 100644
--- a/ppi_py/datasets/datasets.py
+++ b/ppi_py/datasets/datasets.py
@@ -27,6 +27,7 @@ def load_dataset(dataset_folder, dataset_name, download=True):
         "galaxies": "1pDLQesPhbH5fSZW1m4aWC-wnJWnp1rGV",
         "gene_expression": "17PwlvAAKeBYGLXPz9L2LVnNJ66XjuyZd",
         "plankton": "1KEk0ZFZ6KiB7_2tdPc5fyBDFNhhJUS_W",
+        "tree_cover": "1pdl66Wyz_RQ2Ef0xLFt-D8CLBc3ATF_B"
     }
     if dataset_name not in dataset_google_drive_ids.keys():
         raise NotImplementedError(
diff --git a/ppi_py/ptd.py b/ppi_py/ptd.py
new file mode 100644
index 0000000..a9426da
--- /dev/null
+++ b/ppi_py/ptd.py
@@ -0,0 +1,207 @@
+import numpy as np
+import pandas as pd
+from statsmodels.regression.linear_model import WLS, RegressionResults
+from statsmodels.stats.weightstats import _zconfint_generic
+from statsmodels.genmod.generalized_linear_model import GLM
+from statsmodels.genmod.families import Binomial
+from statsmodels.genmod.families.links import Logit
+import statsmodels
+from tqdm import tqdm
+
+'''
+HELPER FUNCTIONS
+'''
+
+def resample_datapoints(data_truth, data_pred, data_pred_unlabeled, w, w_unlabeled):
+    '''
+    Resamples datasets and weights with replacement (to be used in bootstrap step).
+    
+    Args:
+        data_truth (List[ndarray]): ground truth labeled data (each ndarray has n rows)
+        data_pred (List[ndarray]): predicted labeled data (each ndarray has n rows)
+        data_pred_unlabeled (List[ndarray]): predicted unlabeled data (each ndarray has N rows)
+        w (ndarray, optional): sample weights for labeled data (length n)
+        w_unlabeled (ndarray, optional): sample weights for unlabeled data (length N)
+        
+    Returns:
+        resampled version of each of the inputs
+    '''
+    n = len(data_truth[0])
+    N = len(data_pred_unlabeled[0])
+    resampled_indices = np.random.choice(np.arange(0, n+N), size=n+N, replace=True)
+    
+    calibration_indices = resampled_indices[resampled_indices < n]
+    data_truth_b = []
+    for data in data_truth:
+        data_truth_b.append(data[calibration_indices])
+    data_pred_b = []
+    for data in data_pred:
+        data_pred_b.append(data[calibration_indices])
+    
+    pred_indices = resampled_indices[resampled_indices >= n] - n
+    data_pred_unlabeled_b = []
+    for data in data_pred_unlabeled:
+        data_pred_unlabeled_b.append(data[pred_indices])
+    
+    if w is None:
+        w_b = None
+    else:
+        w_b = w[calibration_indices]
+        
+    if w_unlabeled is None:
+        w_unlabeled_b = None
+    else:
+        w_unlabeled_b = w_unlabeled[pred_indices]
+    
+    return data_truth_b, data_pred_b, data_pred_unlabeled_b, w_b, w_unlabeled_b
+
+'''
+MAIN PTD BOOTSTRAP FUNCTION
+'''
+
+def ptd_bootstrap(algorithm, data_truth, data_pred, data_pred_unlabeled, w=None, w_unlabeled=None, B=2000, alpha=0.05, tuning_method='optimal_diagonal'):
+    """
+    Computes tuning matrix, point estimates, and confidence intervals for regression coefficients using the Predict-then-Debias bootstrap algorithm from Kluger et al. (2025), 'Prediction-Powered Inference with Imputed Covariates and Nonuniform Sampling,' <https://arxiv.org/abs/2501.18577>.
+    
+    Args:
+        algorithm (function): python function that takes in data and weights, and returns array containing parameters of interest (e.g., a function that computes linear regression or logistic regression coefficients)
+        data_truth (List[ndarray]): ground truth labeled data (each ndarray has n rows)
+        data_pred (List[ndarray]): predicted labeled data (each ndarray has n rows)
+        data_pred_unlabeled (List[ndarray]): predicted unlabeled data (each ndarray has N rows)
+        w (ndarray, optional): sample weights for labeled data (length n)
+        w_unlabeled (ndarray, optional): sample weights for unlabeled data (length N)
+        B (int, optional): number of bootstrap steps
+        alpha (float, optional): error level (must be in the range (0, 1)). The PTD confidence interval will target a coverage of 1 - alpha. 
+        tuning_method (str, optional): method used to create the tuning matrix: "optimal_diagonal", "optimal", or None. (If tuning_method is None, the identity matrix is used.) 
+        
+    Returns:
+        ndarray: the tuning matrix (dimensions d x d) computed from the selected tuning method
+        ndarray: PTD point estimate of the parameters of interest (length d)
+        tuple: lower and upper bounds of PTD confidence intervals with (1-alpha) coverage
+    """
+    coeff_calibration_list = []
+    coeff_pred_calibration_list = []
+    coeff_pred_unlabeled_list = []
+    
+    # compute bootstrap coefficient estimates 
+    for b in range(B):
+        data_truth_b, data_pred_b, data_pred_unlabeled_b, w_b, w_unlabeled_b = resample_datapoints(data_truth, data_pred, data_pred_unlabeled, w, w_unlabeled)
+
+        coeff_calibration = algorithm(data_truth_b, w_b)
+        coeff_calibration_list.append(coeff_calibration)
+
+        coeff_pred_calibration = algorithm(data_pred_b, w_b)
+        coeff_pred_calibration_list.append(coeff_pred_calibration)
+        
+        coeff_pred_unlabeled = algorithm(data_pred_unlabeled_b, w_unlabeled_b)
+        coeff_pred_unlabeled_list.append(coeff_pred_unlabeled)
+
+    coeff_calibration_list = np.array(coeff_calibration_list)
+    coeff_pred_calibration_list = np.array(coeff_pred_calibration_list)
+    coeff_pred_unlabeled_list = np.array(coeff_pred_unlabeled_list)
+    
+    # compute tuning matrix
+    d = coeff_calibration_list.shape[1]
+    if tuning_method is None:
+        tuning_matrix = np.identity(d)
+    else:
+        cross_cov_calibration = np.atleast_2d(np.cov(np.concatenate((coeff_calibration_list.T, coeff_pred_calibration_list.T)))[:d, d:])
+        cov_pred_calibration = np.atleast_2d(np.cov(coeff_pred_calibration_list.T))
+        cov_pred_unlabeled = np.atleast_2d(np.cov(coeff_pred_unlabeled_list.T))
+        if tuning_method == "optimal":
+            tuning_matrix = cross_cov_calibration @ np.linalg.inv(cov_pred_calibration + cov_pred_unlabeled)
+        elif tuning_method == "optimal_diagonal":
+            tuning_matrix = np.diag(np.diag(cross_cov_calibration)/(np.diag(cov_pred_calibration) + np.diag(cov_pred_unlabeled)))
+            
+    # PTD point estimate
+    coeff_calibration = algorithm(data_truth, w)
+    coeff_pred_calibration = algorithm(data_pred, w)
+    coeff_pred_unlabeled = algorithm(data_pred_unlabeled, w_unlabeled)
+    ptd_pointestimate = coeff_pred_unlabeled @ tuning_matrix.T + (coeff_calibration - coeff_pred_calibration @ tuning_matrix.T)
+    
+    # PTD confidence interval
+    # compute B point estimates using the bootstrap coefficient estimates and tuning matrix 
+    pointestimates = coeff_pred_unlabeled_list @ tuning_matrix.T + (coeff_calibration_list - coeff_pred_calibration_list @ tuning_matrix.T)
+    # compute lower and upper bounds for PTD confidence interval with (1-alpha) coverage
+    lo = np.percentile(pointestimates, 100*alpha/2, axis=0)
+    hi = np.percentile(pointestimates, 100*(1-alpha/2), axis=0)
+    ptd_ci = (lo, hi)
+    
+    return tuning_matrix, ptd_pointestimate, ptd_ci
+
+'''
+LINEAR REGRESSION
+'''
+
+def algorithm_linear_regression(data, w):
+    X, Y = data
+    if w is None:
+        regression = WLS(endog=Y, exog=X).fit()
+    else:
+        regression = WLS(endog=Y, exog=X, weights=w).fit()
+    coeff = regression.params
+    return coeff
+
+def ptd_linear_regression(X, Xhat, Xhat_unlabeled, Y, Yhat, Yhat_unlabeled, w=None, w_unlabeled=None, B=2000, alpha=0.05, tuning_method='optimal_diagonal'):
+    """
+    Computes tuning matrix, point estimates, and confidence intervals for linear regression coefficients using the Predict-then-Debias bootstrap algorithm. 
+    
+    Args:
+        X (ndarray): ground truth covariates in labeled data (dimensions n x p)
+        Xhat (ndarray): predicted covariates in labeled data (dimensions n x p)
+        Xhat_unlabeled (ndarray): predicted covariates in unlabeled data (dimensions N x p)
+        Y (ndarray): ground truth response variable in labeled data (dimensions n x 1)
+        Yhat (ndarray): predicted response variable in labeled data (dimensions n x 1)
+        Yhat_unlabeled (ndarray): predicted response variable in unlabeled data (dimensions N x 1)
+        w (ndarray, optional): sample weights for labeled data (length n)
+        w_unlabeled (ndarray, optional): sample weights for unlabeled data (length N)
+        B (int, optional): number of bootstrap steps
+        alpha (float, optional): error level (must be in the range (0, 1)). The PTD confidence interval will target a coverage of 1 - alpha. 
+        tuning_method (str, optional): method used to create the tuning matrix: "optimal_diagonal", "optimal", or None. (If tuning_method is None, the identity matrix is used.) 
+        
+    Returns:
+        ndarray: the tuning matrix (dimensions d x d) computed from the selected tuning method
+        ndarray: PTD point estimate of the regression coefficients (length d)
+        tuple: lower and upper bounds of PTD confidence intervals with (1-alpha) coverage
+    """
+    data_truth = [X, Y]
+    data_pred = [Xhat, Yhat]
+    data_pred_unlabeled = [Xhat_unlabeled, Yhat_unlabeled]
+    return ptd_bootstrap(algorithm_linear_regression, data_truth, data_pred, data_pred_unlabeled, w=w, w_unlabeled=w_unlabeled, B=B, alpha=alpha, tuning_method=tuning_method)
+
+'''
+LOGISTIC REGRESSION
+'''
+
+def algorithm_logistic_regression(data, w):
+    X, Y = data
+    regression = GLM(endog=Y, exog=X, freq_weights=w, family=Binomial(link=Logit())).fit()
+    coeff = regression.params
+    return coeff
+
+def ptd_logistic_regression(X, Xhat, Xhat_unlabeled, Y, Yhat, Yhat_unlabeled, w=None, w_unlabeled=None, B=2000, alpha=0.05, tuning_method='optimal_diagonal'):
+    """
+    Computes tuning matrix, point estimates, and confidence intervals for logistic regression coefficients using the Predict-then-Debias bootstrap algorithm. 
+    
+    Args:
+        X (ndarray): ground truth covariates in labeled data (dimensions n x p)
+        Xhat (ndarray): predicted covariates in labeled data (dimensions n x p)
+        Xhat_unlabeled (ndarray): predicted covariates in unlabeled data (dimensions N x p)
+        Y (ndarray): ground truth response variable in labeled data (dimensions n x 1)
+        Yhat (ndarray): predicted response variable in labeled data (dimensions n x 1)
+        Yhat_unlabeled (ndarray): predicted response variable in unlabeled data (dimensions N x 1)
+        w (ndarray, optional): sample weights for labeled data (length n)
+        w_unlabeled (ndarray, optional): sample weights for unlabeled data (length N)
+        B (int, optional): number of bootstrap steps
+        alpha (float, optional): error level (must be in the range (0, 1)). The PTD confidence interval will target a coverage of 1 - alpha. 
+        tuning_method (str, optional): method used to create the tuning matrix: "optimal_diagonal", "optimal", or None. (If tuning_method is None, the identity matrix is used.) 
+        
+    Returns:
+        ndarray: the tuning matrix (dimensions d x d) computed from the selected tuning method
+        ndarray: PTD point estimate of the regression coefficients (length d)
+        tuple: lower and upper bounds of PTD confidence intervals with (1-alpha) coverage
+    """
+    data_truth = [X, Y]
+    data_pred = [Xhat, Yhat]
+    data_pred_unlabeled = [Xhat_unlabeled, Yhat_unlabeled]
+    return ptd_bootstrap(algorithm_logistic_regression, data_truth, data_pred, data_pred_unlabeled, w=w, w_unlabeled=w_unlabeled, B=B, alpha=alpha, tuning_method=tuning_method)
\ No newline at end of file
diff --git a/tests/test_ptd.py b/tests/test_ptd.py
new file mode 100644
index 0000000..0e80b9c
--- /dev/null
+++ b/tests/test_ptd.py
@@ -0,0 +1,107 @@
+import numpy as np
+import statsmodels.api as sm
+from tqdm import tqdm
+from concurrent.futures import ProcessPoolExecutor, as_completed
+
+from ppi_py import *
+
+"""
+    PTD test for logistic regression
+"""
+
+
+def ptd_logistic_ci_subtest(alphas, n, N, d):
+    includeds = np.zeros(len(alphas))
+    # Make a synthetic regression problem
+    X = np.random.randn(n, d)
+    beta = np.random.randn(d)
+    beta_prediction = beta + np.random.randn(d) + 2
+    Y = np.random.binomial(1, expit(X.dot(beta)))
+    Yhat = expit(X.dot(beta_prediction))
+    # Make a synthetic unlabeled data set with predictions Yhat
+    X_unlabeled = np.random.randn(N, d)
+    Yhat_unlabeled = expit(X_unlabeled.dot(beta_prediction))
+
+    for j in range(len(alphas)):
+        _, _, beta_ppi_ci = ptd_logistic_regression(
+            X,
+            X,
+            X_unlabeled,
+            Y,
+            Yhat,
+            Yhat_unlabeled,
+            B=1000,
+            alpha=alphas[j],
+        )
+        includeds[j] += int(
+            (beta_ppi_ci[0][0] <= beta[0]) & (beta[0] <= beta_ppi_ci[1][0])
+        )
+    return includeds
+
+
+def test_ptd_logistic_ci_parallel():
+    n = 100
+    N = 1000
+    d = 1
+    alphas = np.array([0.05, 0.1, 0.2])
+    epsilon = 0.1
+    num_trials = 100
+
+    total_includeds = np.zeros(len(alphas))
+
+    with ProcessPoolExecutor() as executor:
+        futures = [
+            executor.submit(
+                ptd_logistic_ci_subtest,
+                alphas,
+                n,
+                N,
+                d,
+            )
+            for i in range(num_trials)
+        ]
+
+        for future in tqdm(as_completed(futures), total=len(futures)):
+            total_includeds += future.result()
+
+    print(total_includeds / num_trials)
+    faileds = [
+        np.any(total_includeds / num_trials < 1 - alphas - epsilon)
+    ]
+    assert not np.any(faileds)
+
+"""
+    PTD test for linear regression
+"""
+def test_ptd_ols_ci():
+    n = 100
+    N = 1000
+    d = 1
+    alphas = np.array([0.05, 0.1, 0.2])
+    epsilon = 0.05
+    num_trials = 100
+    includeds = np.zeros_like(alphas)
+    for i in tqdm(range(num_trials)):
+        # Make a synthetic regression problem
+        X = np.random.randn(n, d)
+        Xhat = X + np.random.randn(n, d)
+        beta = np.random.randn(d)
+        beta_prediction = beta + np.random.randn(d) + 2
+        Y = X.dot(beta) + np.random.randn(n)
+        Yhat = X.dot(beta_prediction) + np.random.randn(n)
+        # Make a synthetic unlabeled data set with predictions Xhat_unlabeled and Yhat_unlabeled
+        X_unlabeled = np.random.randn(N, d)
+        Xhat_unlabeled = X_unlabeled + np.random.randn(N, d)
+        Yhat_unlabeled = X_unlabeled.dot(beta_prediction) + np.random.randn(N)
+        for j in range(alphas.shape[0]):
+            # Compute the confidence interval
+            _, _, beta_ppi_ci = ptd_linear_regression(
+                X, Xhat, Xhat_unlabeled, Y, Yhat, Yhat_unlabeled, B=200, alpha=alphas[j]
+            )
+            # Check that the confidence interval contains the true beta
+            includeds[j] += int(
+                (beta_ppi_ci[0][0] <= beta[0]) & (beta[0] <= beta_ppi_ci[1][0])
+            )
+    print((includeds / num_trials))
+    failed = np.any((includeds / num_trials) < (1 - alphas - epsilon))
+    assert not failed
\ No newline at end of file