gptr is the R package; Peter is your agent inside R.
Ask Peter with peter() to work with your live objects. A
conversational model returns a session; a typed decision model returns a
vector that ordinary R code can use. The name connects readable programs
with the study of reasoning.
Peter Naur: make the program readable
Peter Naur was a Danish computer scientist and a central contributor to ALGOL 60. He edited its language report, which used the syntax notation now known as Backus-Naur Form to describe how programs are constructed. This was collaborative work, including John Backus; Naur helped make the language’s definition precise and readable. The revised ALGOL 60 report records this collaborative work and shows the notation in use.
For gptr, the connection is practical: an analysis should leave code you can read, inspect and run. Peter works inside R, and a designated history document can retain the R code an agent executed beside the prompt that requested it. Recording preserves work for review; replay still depends on the code’s inputs, packages and side effects.
Peter Cathcart Wason: examine the reasoning
Peter Cathcart Wason studied human reasoning. With Jonathan St. B. T. Evans, he proposed a dual-process account in “Dual processes in reasoning?”. Their study distinguished processes behind people’s responses from the conscious reasons people gave for them. An explanation could be a justification rather than an accurate account of how a response arose.
That influence suggests a useful discipline for model-assisted analysis: examine the result and the evidence, as well as the explanation.
Two ideas in one R workflow
gptr uses System 1 for typed logical decisions, choices and scores with probabilities, and System 2 for conversational agents that produce text and request tools. These are software roles inspired by reasoning research; they do not establish that models reproduce human cognition or that Wason introduced those labels.
Use typed decisions to screen or route inputs, keeping uncertainty visible. Use agent sessions to inspect objects and carry out requested R work. Keep useful work in a readable history document, and verify the calculations and outputs before relying on a model-generated explanation.