Applied Examples

This section provides illustrative applications of delicatessen based on replications of examples from textbooks, published scientific articles, and custom examples. These examples are somewhat organized by the topics being covered. If there are applications you would like to see added to this documentations, please contact us.

Essentials

• Ross et al. (2024): Introduction to M-estimation
• Boos & Stefanski (2013): M-Estimation (Estimating Equations)
• Zivich et al. (2022): Life-Science Examples
• High School and Beyond (1982): Clustered Data
• Over-Identified Parameters

Missing Data

• Cole et al. (2022): Addressing Missing Outcome Data
• Cole et al. (2023): Sensitivity Analysis for Missing Data

Survival Analysis

• Collett (2015): Survival Analysis
• Zivich (2026): Pooled Logistic Regression for Survival Analysis

Physical & Natural Sciences

• Hardy-Weinberg Equilibrium
• Spielman et al. (1993): Transmission/Disequilibrium Test
• Michelson (1880): Speed of Light
• Hubble (1929): Expansion of the Universe
• Astrostatistics - Quasar Colorshifts

Biochemistry & Pharmacology

• Bonate (2011): Pharmacokinetic-Pharmacodynamic Modeling
• O’Reilly et al. (1963): Warfarin Pharmacokinetics
• Treloar (1974): Michaelis-Menten Enzyme Kinetic Model
• Ash & Hughes-Oliver (2022): Confidence Bands for Hit Enrichment Curves

Epidemiology & Causal Inference

• Hernan & Robins (2023): Causal Inference with Models
• Morris et al. (2022): Precision in Randomized Trials
• Coffman et al. (2021): Causal Mediation Analysis
• Cole et al. (2023): Fusion Designs
• Richardson et al. (2023): Bespoke Instrumental Variables
• Ding (2024) Chapter 25: Mendelian Randomization

Econometrics & Social Science

• Mroz (1987): Regression and Instrumental Variables
• Agresti & Finlay (2009): Robust Regression
• Cameron & Trivedi (1998): Modeling Count Data

Advanced Modeling

• Silverman (1985): Generalized Additive Model
• Zivich et al. (2025): An Introduction to Confidence Bands
• Colditz (1994): Meta-Analysis
• Function-on-Scalar Regression