<?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>amaendle.r-universe.dev</title><link>https://amaendle.r-universe.dev</link><description>Recent package updates in amaendle</description><generator>R-universe</generator><image><url>https://github.com/amaendle.png</url><title>R packages by amaendle</title><link>https://amaendle.r-universe.dev</link></image><lastBuildDate>Mon, 13 Apr 2026 08:08:58 GMT</lastBuildDate><item><title>[amaendle] dsPUcopula 0.1.0</title><author>maendle@leibniz-bips.de (Andreas Mändle)</author><description>Implements the server-side components required to generate
privacy-protecting synthetic data in the DataSHIELD
infrastructure. The package orchestrates the preprocessing,
copula fitting, synthetic data generation and privacy scoring
workflows by combining R and Python tooling.</description><link>https://github.com/r-universe/amaendle/actions/runs/26221228022</link><pubDate>Mon, 13 Apr 2026 08:08:58 GMT</pubDate><r:package>dsPUcopula</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://amaendle.r-universe.dev</r:repository><r:upstream>https://github.com/amaendle/dsPUcopula</r:upstream></item><item><title>[amaendle] PUcopulaSynth 0.1.0</title><author>maendle@uni-bremen.de (Andreas Mändle)</author><description>Fit multivariate distributions using a Partition-of-Unity
copula dependence structure, estimate marginals, and generate
synthetic data with factor pre/post-processing.</description><link>https://github.com/r-universe/amaendle/actions/runs/25624849354</link><pubDate>Fri, 10 Apr 2026 13:58:19 GMT</pubDate><r:package>PUcopulaSynth</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://amaendle.r-universe.dev</r:repository><r:upstream>https://github.com/amaendle/PUcopulaSynth</r:upstream><r:article><r:source>pu-workflow.Rmd</r:source><r:filename>pu-workflow.html</r:filename><r:title>PUcopulaSynth: end-to-end workflow</r:title><r:created>2025-10-29 10:18:41</r:created><r:modified>2026-03-27 16:53:25</r:modified></r:article></item><item><title>[amaendle] mvGoF 0.0.0.9000</title><author>andreas.maendle@uni-oldenburg.de (Andreas Maendle)</author><description>Functions for the computation of several goodness of fit
statistics. Currently impemented are multivariate versions of
well known EDF statistics: The Anderson-Darling and Cramer-von
Mises statistics for multivariate uniformly(0,1) distributed
data and the bivariate Kolmogorov- Smirnov statistic for (0,1)
distributed data. Further Rosenblatt's transformation for
multivariate normally distributed data is implemented, such
that these tests can be applied as a test for normality.</description><link>https://github.com/r-universe/amaendle/actions/runs/26747239680</link><pubDate>Thu, 02 Apr 2026 11:29:11 GMT</pubDate><r:package>mvGoF</r:package><r:version>0.0.0.9000</r:version><r:status>success</r:status><r:repository>https://amaendle.r-universe.dev</r:repository><r:upstream>https://github.com/amaendle/mvGoF</r:upstream></item><item><title>[bips-hb] ggiraphAlluvial 0.1.1</title><author>maendle@leibniz-bips.de (Andreas Mändle)</author><description>Provides interactive extensions of alluvial geoms from the
'ggalluvial' package for use with 'ggiraph'. The package
enables tooltips, hover effects, and clickable elements for
alluvial plots created with 'ggplot2', making it easier to
explore categorical flow data in interactive visualizations.</description><link>https://github.com/r-universe/bips-hb/actions/runs/26740791072</link><pubDate>Tue, 31 Mar 2026 10:56:39 GMT</pubDate><r:package>ggiraphAlluvial</r:package><r:version>0.1.1</r:version><r:status>success</r:status><r:repository>https://bips-hb.r-universe.dev</r:repository><r:upstream>https://github.com/bips-hb/ggiraphAlluvial</r:upstream><r:article><r:source>getting-started.Rmd</r:source><r:filename>getting-started.html</r:filename><r:title>Getting started with ggiraphAlluvial</r:title><r:created>2026-03-20 18:07:18</r:created><r:modified>2026-03-20 18:07:18</r:modified></r:article></item><item><title>[amaendle] ggiraphAlluvial 0.1.1</title><author>maendle@leibniz-bips.de (Andreas Mändle)</author><description>Provides interactive extensions of alluvial geoms from the
'ggalluvial' package for use with 'ggiraph'. The package
enables tooltips, hover effects, and clickable elements for
alluvial plots created with 'ggplot2', making it easier to
explore categorical flow data in interactive visualizations.</description><link>https://github.com/r-universe/amaendle/actions/runs/25663874229</link><pubDate>Tue, 31 Mar 2026 10:46:52 GMT</pubDate><r:package>ggiraphAlluvial</r:package><r:version>0.1.1</r:version><r:status>success</r:status><r:repository>https://amaendle.r-universe.dev</r:repository><r:upstream>https://github.com/amaendle/ggiraphAlluvial</r:upstream><r:article><r:source>getting-started.Rmd</r:source><r:filename>getting-started.html</r:filename><r:title>Getting started with ggiraphAlluvial</r:title><r:created>2026-03-20 18:07:18</r:created><r:modified>2026-03-20 18:07:18</r:modified></r:article></item><item><title>[bips-hb] dsDashboard 0.4.1</title><author>maendle@leibniz-bips.de (Andreas M\u00e4ndle)</author><description>Provides a framework for building interactive dashboards
in R.  The package is designed to support data analysis both on
local data and on data on a DataSHIELD server.</description><link>https://github.com/r-universe/bips-hb/actions/runs/26088919297</link><pubDate>Fri, 20 Mar 2026 17:00:56 GMT</pubDate><r:package>dsDashboard</r:package><r:version>0.4.1</r:version><r:status>success</r:status><r:repository>https://bips-hb.r-universe.dev</r:repository><r:upstream>https://github.com/bips-hb/dsDashboard</r:upstream></item><item><title>[amaendle] PUcopula 0.1.1</title><author>maendle@uni-bremen.de (Andreas Maendle)</author><description>Structures for creating some types of partition of unity
copula. Based on joint work with Dietmar Pfeifer and Olena
Ragulina.</description><link>https://github.com/r-universe/amaendle/actions/runs/26078746597</link><pubDate>Thu, 19 Mar 2026 21:58:35 GMT</pubDate><r:package>PUcopula</r:package><r:version>0.1.1</r:version><r:status>success</r:status><r:repository>https://amaendle.r-universe.dev</r:repository><r:upstream>https://github.com/amaendle/PUcopula</r:upstream></item><item><title>[amaendle] dsPUcopulaClient 0.1.0</title><author>maendle@leibniz-bips.de (Andreas Mändle)</author><description>Provides client-side helper functions for interacting with
the dsPUcopula DataSHIELD server package. The helpers wrap the
remote procedures for fitting partition-of-unity copula models,
estimating marginal distributions, simulating copula-based
samples and retrieving synthetic data in a privacy-preserving
manner.</description><link>https://github.com/r-universe/amaendle/actions/runs/26156901448</link><pubDate>Tue, 07 Oct 2025 13:42:12 GMT</pubDate><r:package>dsPUcopulaClient</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://amaendle.r-universe.dev</r:repository><r:upstream>https://github.com/bips-hb/dsPUcopulaClient</r:upstream></item><item><title>[bips-hb] IDEFICS.scalc 0.1.0</title><author>maendle@leibniz-bips.de (Andreas Mändle)</author><description>Provides tools to compute standardized percentiles and
z-scores for anthropometric and metabolic parameters in
children, based on age-, sex-, and height-specific reference
data from the IDEFICS study. Supports computation of a
composite Metabolic Syndrome (MetS) score and associated action
levels for child health monitoring.</description><link>https://github.com/r-universe/bips-hb/actions/runs/25957319557</link><pubDate>Wed, 02 Jul 2025 15:09:59 GMT</pubDate><r:package>IDEFICS.scalc</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://bips-hb.r-universe.dev</r:repository><r:upstream>https://github.com/bips-hb/IDEFICS_scalc</r:upstream></item><item><title>[amaendle] mvTargetOpt 0.2.0</title><author>maendle@uni-bremen.de (Andreas Maendler)</author><description>Multi-objective optimization algorithm. A new approach,
using ideas from PCA dimension reduction and PLS regression.</description><link>https://github.com/r-universe/amaendle/actions/runs/26025565299</link><pubDate>Mon, 08 Jun 2020 15:34:39 GMT</pubDate><r:package>mvTargetOpt</r:package><r:version>0.2.0</r:version><r:status>success</r:status><r:repository>https://amaendle.r-universe.dev</r:repository><r:upstream>https://github.com/amaendle/mvTargetOpt</r:upstream><r:article><r:source>introduction.Rmd</r:source><r:filename>introduction.html</r:filename><r:title>Multivariate Multi-Target optimization</r:title><r:created>2020-02-07 13:53:52</r:created><r:modified>2020-02-07 13:53:52</r:modified></r:article></item><item><title>[amaendle] mvInterpolation 0.0.0.9000</title><author>maendle@uni-bremen.de (Andreas Maendle)</author><description>This package implements multivariate extensions of the
interpolation algorithms from Shepard, Donald (1968): &quot;A
two-dimensional interpolation function for irregularly-spaced
data&quot;. Proceedings of the 1968 ACM National Conference. pp.
517&lt;96&gt;524. doi:10.1145/800186.810616.</description><link>https://github.com/r-universe/amaendle/actions/runs/26025580637</link><pubDate>Fri, 19 Jan 2018 14:44:12 GMT</pubDate><r:package>mvInterpolation</r:package><r:version>0.0.0.9000</r:version><r:status>success</r:status><r:repository>https://amaendle.r-universe.dev</r:repository><r:upstream>https://github.com/amaendle/mvInterpolation</r:upstream></item></channel></rss>