<?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>pgajer.r-universe.dev</title><link>https://pgajer.r-universe.dev</link><description>Recent package updates in pgajer</description><generator>R-universe</generator><image><url>https://github.com/pgajer.png</url><title>R packages by pgajer</title><link>https://pgajer.r-universe.dev</link></image><lastBuildDate>Thu, 08 Oct 2026 19:38:27 GMT</lastBuildDate><item><title>[pgajer] geosmooth 0.2.0</title><author>pgajer@gmail.com (Pawel Gajer)</author><description>Provides geometric methods for nonparametric regression
and density estimation on data represented as coordinate
matrices or weighted graphs. Methods include local polynomial
smoothing, model-averaged local polynomial smoothing, local
polynomial lifting trend filtering, synchronized local
polynomial lifting trend filtering, graph low-pass filtering,
and Hessian-energy regression. Methodological references
include Gajer and Ravel (2025) &quot;Adaptive Geometric Regression
for High-Dimensional Structured Data&quot;
&lt;doi:10.48550/arXiv.2511.03817&gt;, Fan and Gijbels (1996,
ISBN:9780412983214), Wang et al. (2016) &quot;Trend Filtering on
Graphs&quot; &lt;https://www.jmlr.org/papers/v17/15-147.html&gt;, and Kim
et al. (2009) &quot;Semi-Supervised Regression Using Hessian Energy&quot;
&lt;https://papers.nips.cc/paper/3741-semi-supervised-regression-using-hessian-energy-with-an-application-to-semi-supervised-dimensionality-reduction&gt;.</description><link>https://github.com/r-universe/pgajer/actions/runs/37853948892</link><pubDate>Thu, 08 Oct 2026 19:38:27 GMT</pubDate><r:package>geosmooth</r:package><r:version>0.2.0</r:version><r:status>success</r:status><r:repository>https://pgajer.r-universe.dev</r:repository><r:upstream>https://github.com/pgajer/geosmooth</r:upstream><r:article><r:source>function-guide.Rmd</r:source><r:filename>function-guide.html</r:filename><r:title>Finding your way around geosmooth</r:title><r:created>2026-09-14 17:44:11</r:created><r:modified>2026-09-15 23:08:19</r:modified></r:article><r:article><r:source>synthetic-datasets.Rmd</r:source><r:filename>synthetic-datasets.html</r:filename><r:title>Synthetic datasets for geometric smoothing</r:title><r:created>2026-07-29 02:23:48</r:created><r:modified>2026-09-15 23:08:19</r:modified></r:article></item><item><title>[pgajer] linf 0.3.1</title><author>pgajer@gmail.com (Pawel Gajer)</author><description>Implements L-infinity normalization for compositional
matrices, assigns samples to dominant features, constructs
truncated and hierarchically refined dominant community state
types, constructs unordered dominance sets with support-based
sample filtering, and computes representative landmark
profiles. The methods are described by Gajer and Ravel (2025)
&lt;doi:10.48550/arXiv.2503.21543&gt;. Bundled vaginal and gut
microbiome data support reproducible demonstrations of the
package interface; phenotype fields in the stratified gut
subset are illustrative and are not suitable for
population-level inference.</description><link>https://github.com/r-universe/pgajer/actions/runs/37237198265</link><pubDate>Sun, 04 Oct 2026 19:43:03 GMT</pubDate><r:package>linf</r:package><r:version>0.3.1</r:version><r:status>success</r:status><r:repository>https://pgajer.r-universe.dev</r:repository><r:upstream>https://github.com/pgajer/linf</r:upstream><r:article><r:source>linf-vaginal.Rmd</r:source><r:filename>linf-vaginal.html</r:filename><r:title>dCSTs for Vaginal Microbiome Data</r:title><r:created>2026-03-25 22:35:00</r:created><r:modified>2026-09-15 14:30:02</r:modified></r:article><r:article><r:source>linf-intro.Rmd</r:source><r:filename>linf-intro.html</r:filename><r:title>Dominant Community State Types: From Normalization to a Gut Demonstration</r:title><r:created>2025-09-25 15:09:01</r:created><r:modified>2026-09-15 16:37:24</r:modified></r:article><r:article><r:source>example-datasets.Rmd</r:source><r:filename>example-datasets.html</r:filename><r:title>Example datasets and reproducible workflows</r:title><r:created>2026-09-15 14:30:02</r:created><r:modified>2026-09-15 23:08:00</r:modified></r:article><r:article><r:source>function-guide.Rmd</r:source><r:filename>function-guide.html</r:filename><r:title>Finding your way around linf</r:title><r:created>2026-09-15 14:30:02</r:created><r:modified>2026-10-04 19:43:03</r:modified></r:article></item><item><title>[pgajer] dgraphs 0.3.0.9000</title><author>pgajer@gmail.com (Pawel Gajer)</author><description>Constructs data-derived graphs from numerical observations
using mutual, shared-neighbor, intersection, geodesic, radius,
adaptive-radius, minimum-spanning-tree completion, and optional
iterated adaptive neighborhood methods. Provides graph
conversion, pruning, diagnostics, spectral embedding, endpoint
detection, and path utilities, with reproducible synthetic
geometries and point sampling for graph and embedding
experiments. The implemented graph constructions include
methods described by Jarvis and Patrick (1973)
&lt;doi:10.1109/T-C.1973.223640&gt;, Brito et al. (1997)
&lt;doi:10.1016/S0167-7152(96)00213-1&gt;, Berry and Sauer (2019)
&lt;doi:10.3934/fods.2019001&gt;, and Gower and Ross (1969)
&lt;doi:10.2307/2346439&gt;.</description><link>https://github.com/r-universe/pgajer/actions/runs/36869874777</link><pubDate>Thu, 01 Oct 2026 10:54:45 GMT</pubDate><r:package>dgraphs</r:package><r:version>0.3.0.9000</r:version><r:status>success</r:status><r:repository>https://pgajer.r-universe.dev</r:repository><r:upstream>https://github.com/pgajer/dgraphs</r:upstream><r:article><r:source>data-derived-graph-workflow.Rmd</r:source><r:filename>data-derived-graph-workflow.html</r:filename><r:title>Constructing and Diagnosing Data-Derived Graphs</r:title><r:created>2026-08-21 01:51:05</r:created><r:modified>2026-09-14 20:23:34</r:modified></r:article><r:article><r:source>fermat-distances.Rmd</r:source><r:filename>fermat-distances.html</r:filename><r:title>Density-sensitive paths with Fermat distances</r:title><r:created>2026-09-30 22:33:55</r:created><r:modified>2026-10-01 01:21:52</r:modified></r:article><r:article><r:source>fermat-surfaces.Rmd</r:source><r:filename>fermat-surfaces.html</r:filename><r:title>Fermat distances and their supporting graphs on surfaces</r:title><r:created>2026-10-01 08:37:21</r:created><r:modified>2026-10-01 10:54:45</r:modified></r:article><r:article><r:source>function-guide.Rmd</r:source><r:filename>function-guide.html</r:filename><r:title>Finding your way around dgraphs</r:title><r:created>2026-09-14 20:23:34</r:created><r:modified>2026-10-01 01:21:52</r:modified></r:article><r:article><r:source>ian-graphs.Rmd</r:source><r:filename>ian-graphs.html</r:filename><r:title>IAN graphs: setup and interpreting results</r:title><r:created>2026-09-24 02:25:04</r:created><r:modified>2026-09-24 02:25:04</r:modified></r:article><r:article><r:source>synthetic-geometry.Rmd</r:source><r:filename>synthetic-geometry.html</r:filename><r:title>Synthetic geometry and point sampling</r:title><r:created>2026-09-14 20:23:34</r:created><r:modified>2026-09-15 22:52:35</r:modified></r:article></item><item><title>[pgajer] grip 0.2.0.9001</title><author>pgajer@gmail.com (Pawel Gajer)</author><description>Implements GRIP multiscale graph layout with a unified
choice between hop-count and geometry-aware edge-length graph
metrics in 2D and 3D. Provides layout scoring, candidate
comparison, multiscale trace diagnostics, synthetic graph
families, and advanced experimental geodesic-KK utilities for
weighted-layout evaluation and polish. Based on Gajer and
Kobourov (2002) &lt;doi:10.7155/jgaa.00052&gt; and Gajer, Goodrich
and Kobourov (2004) &lt;doi:10.1016/j.comgeo.2004.03.014&gt;.
Includes derived graph datasets from the SuiteSparse Matrix
Collection, with source attribution and transformation
metadata.</description><link>https://github.com/r-universe/pgajer/actions/runs/36766601251</link><pubDate>Wed, 30 Sep 2026 14:12:46 GMT</pubDate><r:package>grip</r:package><r:version>0.2.0.9001</r:version><r:status>failure</r:status><r:repository>https://pgajer.r-universe.dev</r:repository><r:upstream>https://github.com/pgajer/grip</r:upstream></item><item><title>[pgajer] ivue 0.1.0</title><author>pgajer@gmail.com (Pawel Gajer)</author><description>Creates interactive three-dimensional point clouds and
embedded weighted graphs with numerical or categorical
annotations. Provides reusable continuous and categorical color
scales, matching legends, highlighting, and geometric edge,
path, and label layers. Supports multiple weighted-graph
formats and optional layouts through 'igraph', with explicit
distance or strength weight semantics. Renders browser widgets
using 'rgl' without requiring a native graphics window. Plays
recorded coordinate frames with interactive controls and
exports orthographic graph animations to GIF.</description><link>https://github.com/r-universe/pgajer/actions/runs/37927202974</link><pubDate>Fri, 18 Sep 2026 18:14:44 GMT</pubDate><r:package>ivue</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://pgajer.r-universe.dev</r:repository><r:upstream>https://github.com/pgajer/ivue</r:upstream><r:article><r:source>example-data.Rmd</r:source><r:filename>example-data.html</r:filename><r:title>Example data and reusable visualization recipes</r:title><r:created>2026-09-15 14:21:33</r:created><r:modified>2026-09-15 16:20:07</r:modified></r:article><r:article><r:source>function-guide.Rmd</r:source><r:filename>function-guide.html</r:filename><r:title>Finding your way around ivue</r:title><r:created>2026-09-15 14:21:33</r:created><r:modified>2026-09-15 23:05:51</r:modified></r:article><r:article><r:source>animation.Rmd</r:source><r:filename>animation.html</r:filename><r:title>Playing and Exporting Coordinate Animations</r:title><r:created>2026-09-04 14:09:54</r:created><r:modified>2026-09-15 23:05:51</r:modified></r:article><r:article><r:source>ivue-introduction.Rmd</r:source><r:filename>ivue-introduction.html</r:filename><r:title>Point Clouds and Weighted Graphs with ivue</r:title><r:created>2026-09-03 13:56:42</r:created><r:modified>2026-09-15 23:05:51</r:modified></r:article><r:article><r:source>retinal-development.Rmd</r:source><r:filename>retinal-development.html</r:filename><r:title>Retinal Development: Point-Cloud and Graph Views</r:title><r:created>2026-09-08 02:03:52</r:created><r:modified>2026-09-15 15:50:41</r:modified></r:article></item></channel></rss>