diff --git a/docs/source/history.md b/docs/source/history.md index cb1f1222..74f27ee3 100644 --- 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 index 1bf4b44c..92ebcc3e 100644 --- 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 index 5b02d37a..a658298d 100644 --- 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 index 693a8839..f90b3fd4 100644 --- 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 index ec9c985e..59ce3362 100644 --- 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 index 0368894a..791edcbe 100644 --- 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 index fe735208..d621a5e0 100644 --- 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 index eb6b2d0d..6f806a47 100644 --- 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 index 42637f1f..78af8e55 100644 --- 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`." ] },