Simon P. Couch
@simonpcouch.com
he/him - writing statistical software at Posit, PBC (née RStudio)🥑
simonpcouch.com, @simonpcouch elsewhere
simonpcouch.com, @simonpcouch elsewhere
side::kick() is model-agnostic. When you first launch the app, a setup flow will detect whether any common providers are already configured to help you get started.
#rstats
#rstats
November 7, 2025 at 12:48 PM
side::kick() is model-agnostic. When you first launch the app, a setup flow will detect whether any common providers are already configured to help you get started.
#rstats
#rstats
I'm excited to share side::kick(), an experimental open-source coding agent for RStudio built entirely in R. It can interact with your files, communicate with your active #rstats session, and run code.
Check it out: github.com/simonpcouch/...
Check it out: github.com/simonpcouch/...
November 5, 2025 at 3:57 PM
I'm excited to share side::kick(), an experimental open-source coding agent for RStudio built entirely in R. It can interact with your files, communicate with your active #rstats session, and run code.
Check it out: github.com/simonpcouch/...
Check it out: github.com/simonpcouch/...
I've been getting a lot more out of Claude Code after hooking it up to tools that let it read #rstats pkg docs. I just type "read about some::topic" before tasking Claude with things that are unlikely to be in its training data, and the model will pull up whatever help files it needs.
June 11, 2025 at 2:15 PM
I've been getting a lot more out of Claude Code after hooking it up to tools that let it read #rstats pkg docs. I just type "read about some::topic" before tasking Claude with things that are unlikely to be in its training data, and the model will pull up whatever help files it needs.
Introducing acquaint, an R package that turns your R sessions into a Model Context Protocol (MCP) server. This allows MCP-enabled tools like Claude Desktop and Claude Code to run #rstats code _in your active R sessions_ to explore objects, read documentation, etc.
posit-dev.github.io/acquaint/
posit-dev.github.io/acquaint/
May 28, 2025 at 3:43 PM
Introducing acquaint, an R package that turns your R sessions into a Model Context Protocol (MCP) server. This allows MCP-enabled tools like Claude Desktop and Claude Code to run #rstats code _in your active R sessions_ to explore objects, read documentation, etc.
posit-dev.github.io/acquaint/
posit-dev.github.io/acquaint/
New #rstats pkg chores on CRAN!
chores is an extensible collection of LLM assistants for R to help you with repetitive, hard-to-automate tasks.
Read more: simonpcouch.github.io/chores/
chores is an extensible collection of LLM assistants for R to help you with repetitive, hard-to-automate tasks.
Read more: simonpcouch.github.io/chores/
February 24, 2025 at 2:35 PM
New #rstats pkg chores on CRAN!
chores is an extensible collection of LLM assistants for R to help you with repetitive, hard-to-automate tasks.
Read more: simonpcouch.github.io/chores/
chores is an extensible collection of LLM assistants for R to help you with repetitive, hard-to-automate tasks.
Read more: simonpcouch.github.io/chores/
New #rstats pkg gander is now on CRAN!
gander is a coding assistant that knows how describe the objects in your global R environment. So, when you're working with data, LLMs will know the names, types, and distributions of data columns, resulting in much more effective completions.
gander is a coding assistant that knows how describe the objects in your global R environment. So, when you're working with data, LLMs will know the names, types, and distributions of data columns, resulting in much more effective completions.
February 20, 2025 at 6:33 PM
New #rstats pkg gander is now on CRAN!
gander is a coding assistant that knows how describe the objects in your global R environment. So, when you're working with data, LLMs will know the names, types, and distributions of data columns, resulting in much more effective completions.
gander is a coding assistant that knows how describe the objects in your global R environment. So, when you're working with data, LLMs will know the names, types, and distributions of data columns, resulting in much more effective completions.