1. What is Statistical Programming?

Statistical programming bridges the gap between data analysis and automation. Whether you’re analyzing complex datasets, simulating models, or building reusable workflows, clean and efficient code ensures that your results are reproducible, transparent, and scalable.

In academic and applied research, statistical programming isn’t just a technical detail — it’s a foundation for good science and streamlined collaboration. I help researchers and teams turn ideas into tools, pipelines, and packages that deliver lasting value.

2. What I Offer

I provide custom programming solutions for research and data science projects — from one-off scripts to full-featured R packages. My work emphasizes readability, reproducibility, and adherence to scientific best practices.

My services include:

  • Writing efficient R or Python code for modeling, simulations, and data processing
  • Automating repetitive or complex workflows
  • Developing and documenting statistical functions
  • Building and publishing custom R packages
  • Supporting open science and collaborative research practices

Typical projects:

  • Simulation engines for power analysis or method validation
  • Custom functions for SEM, longitudinal models, or experimental data
  • Interactive wrappers or APIs for modeling tools
  • Git-based workflow and documentation for reproducibility

3. Consulting Packages

Whether you need one script or a full-featured package, I offer flexible, collaborative packages tailored to your scope and goals.

Script & Workflow Support

Development of custom code for:

  • Modeling pipelines
  • Simulation studies
  • Reproducible preprocessing and analysis

Great for teams looking to automate parts of their analysis or standardize a process.

Package Development

Design and development of a custom R package:

  • Function design and implementation
  • Testing and documentation
  • Optional: website/manual using pkgdown or GitHub Pages
  • Support for CRAN submission (if applicable)

Ideal for labs, research projects, or teaching teams who want reusable, shareable tools.

Ongoing Programming Support

A collaborative, long-term option for:

  • Maintaining or extending existing codebases
  • Refactoring legacy code
  • Developing statistical tools over time

Best for research groups or data teams who need continuous development and support.

4. Who This Is For

  • Researchers who want to automate repetitive analysis tasks
  • Labs developing tools for wider academic use
  • Applied data teams that need scalable, documented code
  • Open science advocates building reusable workflows

5. Why Work With Me?

I’ve developed and published several R packages (e.g., semnova, subgroupSEM, powerANOVA, sccm) that support reproducible and advanced statistical analysis. My work balances usability, transparency, and methodological accuracy — always written with researchers and collaborators in mind.

With a background in both statistical methodology and software development, I bring a rare combination of technical fluency and research insight.

6. Ready to Build Something?

Whether it’s a single function or a complete statistical package, I’m happy to help. Let’s talk about your needs and design a package that fits.