USEPA · cwsimmon · Oct 17, 2025 · Oct 14, 2025 · Oct 14, 2025 · Oct 14, 2025
diff --git a/docs/source/history.md b/docs/source/history.md
@@ -27,6 +27,16 @@
 
 ## pybmds
 
+### Version 25.2 
+
+*Released on 2025-10-xx.*
+
+* Added Cochran Armitage trend test for dichotomous data
+* Added Jonckheere-Terpstra trend test for continuous data
+* Added additional plotting functionality for nested dichotomous data
+* Changed restriction for rho parameter in non-constant variance model to allow negative values
+* Added ability to count all parameters in a model automatically for the purpose of calculating AIC and p-values.
+
 ### Version 25.1
 
 *Released on 2025-04-25.*

diff --git a/docs/source/recipes/batch.ipynb b/docs/source/recipes/batch.ipynb
@@ -1,5 +1,20 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0d9e0c38",
+   "metadata": {},
+   "source": [
+    "# Batch Execution of Analyses\n",
+    "\n",
+    "## Table of Contents\n",
+    "\n",
+    "- [Batch execution](#batch-execution)\n",
+    "- [Single model, multiple datasets](#single-model-multiple-datasets)\n",
+    "- [Session batch execution](#session-batch-execution)\n",
+    "- [Batch running trend tests](#batch-running-trend-tests)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -26,7 +41,7 @@
     "tags": []
    },
    "source": [
-    "# Batch Execution\n",
+    "## Batch execution\n",
     "\n",
     "If you have several dose-response datasets, you can run them as a batch.  \n",
     "\n",
@@ -324,6 +339,14 @@
     "    print(f\"Dataset {i+1} Results:\")\n",
     "    print(trend_result.tbl())\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf598584",
+   "metadata": {},
+   "source": [
+    "Note that, conceptually, a batch approach can be used to run the Jonckheere-Terpstra trend test as well.  However, given that the exact Jonckheere-Terpstra trend test uses a permutation approach to calculate the p-value, the computational burden can be large and memory issues may arise if a large number of datasets are run in a batch fashion.  Caution is advised when running the exact Jonckheere-Terpstra trend test in a batch fashion.  If batch analysis is required, it may be adventageous to use the approximate trend test instead."
+   ]
   }
  ],
  "metadata": {

diff --git a/docs/source/recipes/continuous.ipynb b/docs/source/recipes/continuous.ipynb
@@ -8,6 +8,21 @@
     "# Continuous Data"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "1699fc4f",
+   "metadata": {},
+   "source": [
+    "## Table of Contents\n",
+    "\n",
+    "- [Quickstart](#quickstart)\n",
+    "- [Continuous datasets](#continuous-datasets)\n",
+    "- [Single model fit](#single-model-fit)\n",
+    "- [Multiple model fit](#multiple-model-fit-sessions-and-model-recommendation)\n",
+    "- [Changing how parameters are counted](#changing-how-parameters-are-counted)\n",
+    "- [Jonckheere-Terpstra trend test](#jonckheere-terpstra-trend-test)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

diff --git a/docs/source/recipes/custom-excel-exports.ipynb b/docs/source/recipes/custom-excel-exports.ipynb
@@ -1,5 +1,18 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ef8d8736",
+   "metadata": {},
+   "source": [
+    "# Creating Custom Excel Exports\n",
+    "\n",
+    "## Table of Contents\n",
+    "\n",
+    "- [Customize an Excel export](#customize-an-excel-export)\n",
+    "- [Model result introspection](#model-result-introspection)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -31,7 +44,7 @@
     "tags": []
    },
    "source": [
-    "# Customize an Excel Export\n",
+    "## Customize an Excel Export\n",
     "\n",
     "After executing a batch analysis, you may want to add some additional information to the default Excel exports. \n",
     "\n",

diff --git a/docs/source/recipes/dichotomous.ipynb b/docs/source/recipes/dichotomous.ipynb
@@ -23,6 +23,15 @@
    "source": [
     "# Dichotomous Data\n",
     "\n",
+    "## Table of Contents\n",
+    "\n",
+    "- [Quickstart](#quickstart)\n",
+    "- [Dichotomous datasets](#dichotomous-datasets)\n",
+    "- [Single model fit](#single-model-fit)\n",
+    "- [Multiple model fit (sessions)](#multiple-model-fit-sessions-and-model-recommendation)\n",
+    "- [Changing how parameter are counted](#changing-how-parameters-are-counted)\n",
+    "- [Cochran-Armitage trend test](#cochran-armitage-trend-test)\n",
+    "\n",
     "## Quickstart\n",
     "\n",
     "To run a dichotomous dataset:"

diff --git a/docs/source/recipes/dichotomous_ma.ipynb b/docs/source/recipes/dichotomous_ma.ipynb
@@ -22,6 +22,11 @@
    "source": [
     "# Dichotomous Data with Bayesian Model Averaging\n",
     "\n",
+    "## Table of Contents\n",
+    "\n",
+    "- [Single model fit](#single-model-fit)\n",
+    "- [Multiple model fit (sessions)](#multiple-model-fit-sessions)\n",
+    "\n",
     "Bayesian model averaging is currently available for dichotomous datasets in `pybmds`. Here, we describe how to run a dichotomous analysis with Bayesian model averaging and how to plot your results. Also, we will demonstrate how you can override the default priors for parameter estimation."
    ]
   },

diff --git a/docs/source/recipes/multitumor.ipynb b/docs/source/recipes/multitumor.ipynb
@@ -27,6 +27,14 @@
    "source": [
     "# Tumor and Multitumor Data\n",
     "\n",
+    "## Table of Contents\n",
+    "\n",
+    "- [Quickstart](#quickstart)\n",
+    "- [Create a tumor dataset](#create-a-tumor-dataset)\n",
+    "- [Single dataset fit](#single-dataset-fit)\n",
+    "- [Multiple dataset fit](#multiple-dataset-fit)\n",
+    "- [PolyK adjustment for early mortality](#polyk-adjustment-for-early-mortality)\n",
+    "\n",
     "Conducting dose-response analysis on dichotomous tumor data differs from analyzing standard dichotomous tumor data in the following ways:\n",
     "\n",
     "* The Multistage cancer model uses different parameter settings for model fit than the standard Multistage model.\n",

diff --git a/docs/source/recipes/nested_dichotomous.ipynb b/docs/source/recipes/nested_dichotomous.ipynb
@@ -7,7 +7,23 @@
     "tags": []
    },
    "source": [
-    "# Nested Dichotomous Data"
+    "# Nested Dichotomous Data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c368b86",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Table of Contents\n",
+    "\n",
+    "- [Quickstart](#quickstart)\n",
+    "- [Nested dichotomous dataset](#nested-dichotomous-dataset)\n",
+    "- [Single model fit](#single-model-fit)\n",
+    "- [Multiple model fit (sessions)](#multiple-model-fit-sessions-and-model-recommendation)\n",
+    "- [Additional nested dichotomous plotting](#additional-nested-dichotomous-plottting)\n",
+    "- [Rao-Scott transformation](#rao-scott-transformation-for-summary-level-data)"
    ]
   },
   {

diff --git a/docs/source/recipes/preparing-datasets.ipynb b/docs/source/recipes/preparing-datasets.ipynb
@@ -27,6 +27,11 @@
    "source": [
     "# Dataset Preparation\n",
     "\n",
+    "## Table of Contents\n",
+    "\n",
+    "- [Processing long datasets](#processing-long-datasets)\n",
+    "- [Processing wide datasets](#processing-wide-datasets)\n",
+    "\n",
     "This section compiles a few patterns that can be used to load data from a tabular file as preparation for modeling in `pybmds`."
    ]
   },