<?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>stavrospap1.r-universe.dev</title><link>https://stavrospap1.r-universe.dev</link><description>Recent package updates in stavrospap1</description><generator>R-universe</generator><image><url>https://github.com/stavrospap1.png</url><title>R packages by stavrospap1</title><link>https://stavrospap1.r-universe.dev</link></image><lastBuildDate>Tue, 01 Sep 2026 14:59:12 GMT</lastBuildDate><item><title>[stavrospap1] MXM 1.5.8</title><author>staurospapflor@gmail.com (Stavros Papadopoulos)</author><description>Many feature selection methods for a wide range of
response variables, including minimal, statistically-equivalent
and equally-predictive feature subsets. Bayesian network
algorithms and related functions are also included. The package
name 'MXM' stands for &quot;Mens eX Machina&quot;, meaning &quot;Mind from the
Machine&quot; in Latin. References: a) Lagani, V. and Athineou, G.
and Farcomeni, A. and Tsagris, M. and Tsamardinos, I. (2017).
&quot;Feature Selection with the R Package MXM: Discovering
Statistically Equivalent Feature Subsets&quot;. Journal of
Statistical Software, 80(7). &lt;doi:10.18637/jss.v080.i07&gt;. b)
Tsagris, M., Lagani, V. and Tsamardinos, I. (2018). &quot;Feature
selection for high-dimensional temporal data&quot;. BMC
Bioinformatics, 19:17. &lt;doi:10.1186/s12859-018-2023-7&gt;. c)
Tsagris, M., Borboudakis, G., Lagani, V. and Tsamardinos, I.
(2018). &quot;Constraint-based causal discovery with mixed data&quot;.
International Journal of Data Science and Analytics, 6(1):
19-30. &lt;doi:10.1007/s41060-018-0097-y&gt;. d) Tsagris, M.,
Papadovasilakis, Z., Lakiotaki, K. and Tsamardinos, I. (2018).
&quot;Efficient feature selection on gene expression data: Which
algorithm to use?&quot; BioRxiv. &lt;doi:10.1101/431734&gt;. e) Tsagris,
M. (2019). &quot;Bayesian Network Learning with the PC Algorithm: An
Improved and Correct Variation&quot;. Applied Artificial
Intelligence, 33(2):101-123.
&lt;doi:10.1080/08839514.2018.1526760&gt;. f) Tsagris, M. and
Tsamardinos, I. (2019). &quot;Feature selection with the R package
MXM&quot;. F1000Research 7: 1505.
&lt;doi:10.12688/f1000research.16216.2&gt;. g) Borboudakis, G. and
Tsamardinos, I. (2019). &quot;Forward-Backward Selection with Early
Dropping&quot;. Journal of Machine Learning Research 20: 1-39. h)
Tsagris, M., Papadovasilakis, Z., Lakiotaki, K. and
Tsamardinos, I. (2022). &quot;The gamma-OMP algorithm for feature
selection with application to gene expression data&quot;. IEEE/ACM
Transactions on Computational Biology and Bioinformatics 19(2):
1214-1224. &lt;doi:10.1109/TCBB.2020.3029952&gt;.</description><link>https://github.com/r-universe/stavrospap1/actions/runs/38071570414</link><pubDate>Tue, 01 Sep 2026 14:59:12 GMT</pubDate><r:package>MXM</r:package><r:version>1.5.8</r:version><r:status>failure</r:status><r:repository>https://stavrospap1.r-universe.dev</r:repository><r:upstream>https://github.com/cran/MXM</r:upstream><r:article><r:source>guide.ltx</r:source><r:filename>guide.pdf</r:filename><r:title>A very brief guide to using MXM</r:title><r:created>2017-10-06 19:48:15</r:created><r:modified>2019-12-06 14:30:18</r:modified></r:article><r:article><r:source>article.ltx</r:source><r:filename>article.pdf</r:filename><r:title>Discovering Statistically-Equivalent Feature Subsets with MXM</r:title><r:created>2016-12-20 09:46:49</r:created><r:modified>2026-09-01 14:59:12</r:modified></r:article><r:article><r:source>FS_guide.ltx</r:source><r:filename>FS_guide.pdf</r:filename><r:title>Guide on performing feature selection with the R package MXM</r:title><r:created>2018-05-24 10:56:05</r:created><r:modified>2026-09-01 14:59:12</r:modified></r:article><r:article><r:source>MMPC_tutorial.Rmd</r:source><r:filename>MMPC_tutorial.html</r:filename><r:title>Tutorial: Feature selection with the MMPC algorithm</r:title><r:created>2018-09-19 13:40:03</r:created><r:modified>2026-09-01 14:59:12</r:modified></r:article><r:article><r:source>SES_KMVerrou_11_12.Rmd</r:source><r:filename>SES_KMVerrou_11_12.html</r:filename><r:title>Tutorial: Feature selection with the SES algorithm</r:title><r:created>2018-03-30 16:07:35</r:created><r:modified>2026-09-01 14:59:12</r:modified></r:article></item></channel></rss>