<?xml version="1.0" encoding="utf-8" ?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:r="https://r-universe.dev"><channel><title>kennispunttwente.r-universe.dev</title><link>https://kennispunttwente.r-universe.dev</link><description>Recent package updates in kennispunttwente</description><generator>R-universe</generator><image><url>https://github.com/kennispunttwente.png</url><title>R packages by kennispunttwente</title><link>https://kennispunttwente.r-universe.dev</link></image><lastBuildDate>Wed, 22 Apr 2026 20:11:55 GMT</lastBuildDate><item><title>[kennispunttwente] tidyprompt 0.4.0.9000</title><author>koningluka@gmail.com (Luka Koning)</author><description>Easily construct prompts and associated logic for
interacting with large language models (LLMs). 'tidyprompt'
introduces the concept of prompt wraps, which are building
blocks that you can use to quickly turn a simple prompt into a
complex one. Prompt wraps do not just modify the prompt text,
but also add extraction and validation functions that will be
applied to the response of the LLM. This ensures that the user
gets the desired output. 'tidyprompt' can add various features
to prompts and their evaluation by LLMs, such as structured
output, automatic feedback, retries, reasoning modes,
autonomous R function calling, and R code generation and
evaluation. It is designed to be compatible with any LLM
provider that offers chat completion.</description><link>https://github.com/r-universe/kennispunttwente/actions/runs/26279201865</link><pubDate>Wed, 22 Apr 2026 20:11:55 GMT</pubDate><r:package>tidyprompt</r:package><r:version>0.4.0.9000</r:version><r:status>success</r:status><r:repository>https://kennispunttwente.r-universe.dev</r:repository><r:upstream>https://github.com/kennispunttwente/tidyprompt</r:upstream><r:article><r:source>creating_prompt_wraps.Rmd</r:source><r:filename>creating_prompt_wraps.html</r:filename><r:title>Creating prompt wraps</r:title><r:created>2024-12-07 17:59:34</r:created><r:modified>2025-02-06 19:25:32</r:modified></r:article><r:article><r:source>getting_started.Rmd</r:source><r:filename>getting_started.html</r:filename><r:title>Getting started</r:title><r:created>2024-11-22 16:29:22</r:created><r:modified>2026-04-16 19:33:17</r:modified></r:article><r:article><r:source>sentiment_analysis.Rmd</r:source><r:filename>sentiment_analysis.html</r:filename><r:title>Sentiment analysis in R with a LLM and 'tidyprompt'</r:title><r:created>2024-11-30 10:25:57</r:created><r:modified>2024-11-30 11:51:46</r:modified></r:article><r:article><r:source>streaming_shiny_ipc.Rmd</r:source><r:filename>streaming_shiny_ipc.html</r:filename><r:title>Streaming LLM responses to Shiny apps</r:title><r:created>2025-11-14 17:04:19</r:created><r:modified>2025-11-30 15:37:13</r:modified></r:article></item><item><title>[kennispunttwente] fabricQueryR 0.2.1.9000</title><author>koningluka@gmail.com (Luka Koning)</author><description>Query data hosted in 'Microsoft Fabric'. Provides helpers
to open 'DBI' connections to 'SQL' endpoints of 'Lakehouse' and
'Data Warehouse' items; submit 'Data Analysis Expressions'
('DAX') queries to semantic model datasets in 'Microsoft
Fabric' and 'Power BI'; read 'Delta Lake' tables stored in
'OneLake' ('Azure Data Lake Storage Gen2'); and execute 'Spark'
code via the 'Livy API'.</description><link>https://github.com/r-universe/kennispunttwente/actions/runs/26881385127</link><pubDate>Fri, 03 Apr 2026 23:43:21 GMT</pubDate><r:package>fabricQueryR</r:package><r:version>0.2.1.9000</r:version><r:status>success</r:status><r:repository>https://kennispunttwente.r-universe.dev</r:repository><r:upstream>https://github.com/kennispunttwente/fabricqueryr</r:upstream></item></channel></rss>