<?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>fwickel1.r-universe.dev</title><link>https://fwickel1.r-universe.dev</link><description>Recent package updates in fwickel1</description><generator>R-universe</generator><image><url>https://github.com/fwickel1.png</url><title>R packages by fwickel1</title><link>https://fwickel1.r-universe.dev</link></image><lastBuildDate>Wed, 26 Aug 2026 07:24:21 GMT</lastBuildDate><item><title>[fwickel1] pks 0.8-0</title><author>wickelmaier@web.de (Florian Wickelmaier)</author><description>Fitting and testing probabilistic knowledge structures,
especially the basic local independence model (BLIM, Doignon &amp;
Flamagne, 1999) and the simple learning model (SLM), using the
minimum discrepancy maximum likelihood (MDML) method (Heller &amp;
Wickelmaier, 2013 &lt;doi:10.1016/j.endm.2013.05.145&gt;).</description><link>https://github.com/r-universe/fwickel1/actions/runs/39113064626</link><pubDate>Wed, 26 Aug 2026 07:24:21 GMT</pubDate><r:package>pks</r:package><r:version>0.8-0</r:version><r:status>success</r:status><r:repository>https://fwickel1.r-universe.dev</r:repository><r:upstream>https://codeberg.org/fwickel/pks</r:upstream><r:article><r:source>EstimationStepByStep.Rnw</r:source><r:filename>EstimationStepByStep.pdf</r:filename><r:title>Parameter Estimation in Probabilistic Knowledge Structures -- Step by Step</r:title><r:created>2023-07-06 14:44:48</r:created><r:modified>2024-09-16 15:57:23</r:modified></r:article></item><item><title>[fwickel1] eba 1.10-1</title><author>wickelmaier@web.de (Florian Wickelmaier)</author><description>Fitting and testing multi-attribute probabilistic choice
models, especially the Bradley-Terry-Luce (BTL) model (Bradley
&amp; Terry, 1952 &lt;doi:10.1093/biomet/39.3-4.324&gt;; Luce, 1959),
elimination-by-aspects (EBA) models (Tversky, 1972
&lt;doi:10.1037/h0032955&gt;), and preference tree (Pretree) models
(Tversky &amp; Sattath, 1979 &lt;doi:10.1037/0033-295X.86.6.542&gt;).</description><link>https://github.com/r-universe/fwickel1/actions/runs/39113056977</link><pubDate>Fri, 18 Apr 2025 16:30:02 GMT</pubDate><r:package>eba</r:package><r:version>1.10-1</r:version><r:status>success</r:status><r:repository>https://fwickel1.r-universe.dev</r:repository><r:upstream>https://github.com/cran/eba</r:upstream></item><item><title>[fwickel1] mpt 1.0-0</title><author>wickelmaier@web.de (Florian Wickelmaier)</author><description>Fitting and testing multinomial processing tree (MPT)
models, a class of nonlinear models for categorical data.  The
parameters are the link probabilities of a tree-like graph and
represent the latent cognitive processing steps executed to
arrive at observable response categories (Batchelder &amp; Riefer,
1999 &lt;doi:10.3758/bf03210812&gt;; Erdfelder et al., 2009
&lt;doi:10.1027/0044-3409.217.3.108&gt;; Riefer &amp; Batchelder, 1988
&lt;doi:10.1037/0033-295x.95.3.318&gt;).</description><link>https://github.com/r-universe/fwickel1/actions/runs/39113059984</link><pubDate>Fri, 11 Oct 2024 10:00:03 GMT</pubDate><r:package>mpt</r:package><r:version>1.0-0</r:version><r:status>success</r:status><r:repository>https://fwickel1.r-universe.dev</r:repository><r:upstream>https://github.com/cran/mpt</r:upstream><r:article><r:source>BasicsIdentifiabilityPower.Rmd</r:source><r:filename>BasicsIdentifiabilityPower.html</r:filename><r:title>MPT Modeling: Basics, Identifiability, Power</r:title><r:created>2024-10-11 10:00:03</r:created><r:modified>2024-10-11 10:00:03</r:modified></r:article></item></channel></rss>