<?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>katjadanielzik.r-universe.dev</title><link>https://katjadanielzik.r-universe.dev</link><description>Recent package updates in katjadanielzik</description><generator>R-universe</generator><image><url>https://github.com/katjadanielzik.png</url><title>R packages by katjadanielzik</title><link>https://katjadanielzik.r-universe.dev</link></image><lastBuildDate>Mon, 05 Oct 2026 13:28:30 GMT</lastBuildDate><item><title>[bioc] MetaboDynamics 2.3.13</title><author>katja.danielzik@uni-due.de (Katja Danielzik)</author><description>MetaboDynamics is an R-package that provides a framework
of probabilistic models to analyze longitudinal metabolomics
data. It enables robust estimation of mean concentrations
despite varying spread between timepoints and reports
differences between timepoints as well as metabolite specific
dynamics profiles that can be used for identifying &quot;dynamics
clusters&quot; of metabolites of similar dynamics. Provides
probabilistic over-representation analysis of KEGG functional
modules and pathways as well as comparison between clusters of
different experimental conditions.</description><link>https://github.com/r-universe/bioc/actions/runs/37339451319</link><pubDate>Mon, 05 Oct 2026 13:28:30 GMT</pubDate><r:package>MetaboDynamics</r:package><r:version>2.3.13</r:version><r:status>success</r:status><r:repository>https://bioc.r-universe.dev</r:repository><r:upstream>https://github.com/bioc/MetaboDynamics</r:upstream><r:article><r:source>MetaboDynamics.Rmd</r:source><r:filename>MetaboDynamics.html</r:filename><r:title>MetaboDynamics: analyzing longitudinal metabolomics data with probabilistic models</r:title><r:created>2024-12-20 08:57:12</r:created><r:modified>2026-10-05 06:33:21</r:modified></r:article><r:article><r:source>MetaboDynamics_dataframes.Rmd</r:source><r:filename>MetaboDynamics_dataframes.html</r:filename><r:title>Using MetaboDynamics with data frames</r:title><r:created>2025-07-23 17:01:01</r:created><r:modified>2026-10-04 18:59:53</r:modified></r:article><r:article><r:source>MetaboDynamics_Guide_to_prior_setting.Rmd</r:source><r:filename>MetaboDynamics_Guide_to_prior_setting.html</r:filename><r:title>Guide to prior setting</r:title><r:created>2026-08-19 15:28:17</r:created><r:modified>2026-10-01 12:38:47</r:modified></r:article></item><item><title>[katjadanielzik] BayesVolcano 1.0.1</title><author>katja.danielzik@uni-due.de (Katja Danielzik)</author><description>Bayesian models are used to estimate effect sizes (e.g.,
gene expression changes, protein abundance differences, drug
response effects) while accounting for uncertainty, small
sample sizes, and complex experimental designs. However,
Bayesian posteriors of models with many parameters are often
difficult to interpret at a glance. One way to quickly identify
important biological changes based on frequentist analysis are
volcano plots (using fold-changes and p-values). Bayesian
volcano plots bring together the explicit treatment of
uncertainty in Bayesian models and the familiar visualization
of volcano plots.</description><link>https://github.com/r-universe/katjadanielzik/actions/runs/37298936405</link><pubDate>Tue, 07 Jul 2026 15:40:38 GMT</pubDate><r:package>BayesVolcano</r:package><r:version>1.0.1</r:version><r:status>success</r:status><r:repository>https://katjadanielzik.r-universe.dev</r:repository><r:upstream>https://github.com/katjadanielzik/bayesvolcano</r:upstream><r:article><r:source>BayesVolcano.Rmd</r:source><r:filename>BayesVolcano.html</r:filename><r:title>BayesVolcano</r:title><r:created>2026-03-21 18:55:57</r:created><r:modified>2026-04-28 10:19:31</r:modified></r:article></item></channel></rss>