Claude Code at Work

How I use it for open-source pharmacometrics

Andy Stein

September 23, 2026

Overview

• When and why I use Claude Code rather than Positron + GitHub Copilot
• How I set up Claude Code
• Four pieces of open-source work I did using Claude Code

When and why I use Claude Code

Advantages of Claude Code over Positron + GitHub Copilot

1. I can use the latest Claude models (Fable).
2. I can control Claude Code from my phone, my home machine, or my work machine with /remote-control.
3. Claude Code runs on my personal machine at home, using ordinary GitHub. No confusion with the GitHub Copilot setup and corporate Github account.
4. Claude Code runs longer and requires less input from user But, better positron settings (Auto) might help (?).

• First use of Fable, it kicked me down to Opus because I was working on “bio.”
• While Fable isn’t needed for standard coding, I believe more sophisticated models may be useful for more complex tasks where standard models aren’t available and careful literature review and novel methods are warranted.

How I set up Claude Code

Getting access

Running Claude directly at claude.ai is blocked until you have done the short internal training that is linked to whenever you hit the block.

Workflow for building website for task

• For an R package, use pkgdown. For anything else, use a site made by quarto_render().
• Sites are published by Continuous Integration (CI) through GitHub Actions.
• I read the rendered output — on my phone, at home, or at work.
• Claude sets all of that up with minimal work from me.

I’ve set up the TrinityMetrics repository for this.

If you want to try it

1. Navigate to claude.ai and do the minimal training required so the site isn’t blocked.
2. Pick a GitHub repository to work in, or create your own
3. Give Claude a small task, and ask it to use GitHub Actions to set up a website and for publishing the results.
4. Type /remote-control in the chat window to run from other machines, including your phone.
5. Check the output.

What was built with Claude Code

• xgxr — fixed a user-reported bug and got the package to CRAN.
• xgx — rebuilt the website to publish by continuous integration.
• synpmx — a package for generating synthetic pharmacometrics datasets.
• TCE-IPDE — a specification for simulating intrapatient dose escalation.

1. xgxr: User reported a bug

1. xgxr: Source of bug was using a hand-copied “fork” of ggplot2 function

• xgxr::StatSummaryBinQuant is a hand-copied fork of ggplot2’s stat-summary-bin.R, pinned in its roxygen block to ggplot2 commit 351eb41. It never picked up a transform step ggplot2 added later.
• In this old version it had: breaks <- breaks
• ggplot2 since changed with breaks <- scales[[x]]$transform(breaks)

1. xgxr: Fixed bug and got package ready for CRAN

• Fixed bug and then updated xgxr to 1.1.6 to CRAN (first update in 3 years)
• All CRAN checks, warnings, and notes were handled by Claude.
• Along the way it noticed a hyperlink whose TLS certificate had expired, which is the kind of thing CRAN rejects a submission for, and removed it unasked

2. xgx using Continuous Integration

Continuous Integration (CI) via GitHub Actions publishes the site.

• xgx was our open source website and we built it using dozens of .html files committed to github.
• A better way to work is with continuous integration (CI) that builds the site after every push so the code is in the github repository but the html is not.
• Github Actions does this and Claude Code set this up for me.
• I am not a Github expert. In the past I relied on experts for help, but now I can quit bugging Matt and do a lot myself.

opensource.nibr.com/xgx

3. synpmx: Package for Generating Synthetic Pharmacometrics Datasets

Allows for workflows, teaching material and tooling can be developed and shared without exposing real clinical data.

This is still a work in progress, with three different synthetic data generators explored so far, each with their own strengths.

• PMX models
• Principle Component Analysis PCA
• AVATAR - Patient blending

iamstein.github.io/synpmx

3. synpmx: Most useful aspects; building a scorecard and a set of datasets to test

• 8 public datasets used to test the generator — warfarin, theophylline, mavoglulant, etc., from nlmixr2data and xgxr package.
• A scorecard and a set of graphical checks for evaluating the synthetic data

The above lets me tell if the syntehtic data looks reasonable.

Once I have an algorithm that works well and the process as acceptable by data privacy, I can validate the code. Thus far, it’s all been “vibe-coded” with Claude Code.

It’s very helpful having a working prototype for building my own understanding for discussing with others.

4. Exploring Strategic Questions

Does intra-patient dose escalation reach a confirmable active dose sooner in a first-in-human T-cell engager trial?

MCLA-117: a published within-patient ladder, 25 µg to 400 mg, transcribed from the EHA 2020 poster.

4. Output is currently a specification.

A specification, a reading queue, and a references page marking, for every source, whether the claim drawn from it had been checked against the source.

Working specification

I’m reading/thinking more about the difference between “vibe-coding” vs “agentic-engineering”, the latter meaning to think more about the analysis plan before building a tool. Guide for creating working specifications

Demo

[live]