<?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>pglpm.r-universe.dev</title><link>https://pglpm.r-universe.dev</link><description>Recent package updates in pglpm</description><generator>R-universe</generator><image><url>https://github.com/pglpm.png</url><title>R packages by pglpm</title><link>https://pglpm.r-universe.dev</link></image><lastBuildDate>Thu, 02 Jul 2026 17:43:25 GMT</lastBuildDate><item><title>[pglpm] prova 1.0.0</title><author>pgl@portamana.org (PierGianLuca Porta Mana)</author><description>Calculate posterior joint and conditional probabilities,
probability distributions of population frequencies, and
information-theoretic measures, by means of Bayesian
nonparametric methods. Data imputation is automatic and done in
a principled way. Markov-chain Monte Carlo calculations are
automatically handled and do not require user supervision.
Applications range from statistical estimation and
probabilistic hypothesis testing to evidence-based inference
and decision making, in a wide range of disciplines from
astrophysics to medicine. For more details and examples see for
instance Porta Mana et al. (2026) &lt;doi:10.31219/osf.io/8nr56&gt;,
Dunson &amp; Bhattacharya (2011)
&lt;doi:10.1093/acprof:oso/9780199694587.003.0005&gt;, Lindley &amp;
Novick (1981) &lt;doi:10.1214/aos/1176345331&gt;, Bernardo &amp; Smith
(2000) &lt;doi:10.1002/9780470316870&gt;, Müller et al. (2015)
&lt;doi:10.1007/978-3-319-18968-0&gt;. Requires the packages
'Nimble', 'parallel', 'extraDistr'.</description><link>https://github.com/r-universe/pglpm/actions/runs/28621455779</link><pubDate>Thu, 02 Jul 2026 17:43:25 GMT</pubDate><r:package>prova</r:package><r:version>1.0.0</r:version><r:status>success</r:status><r:repository>https://pglpm.r-universe.dev</r:repository><r:upstream>https://github.com/pglpm/prova</r:upstream><r:article><r:source>intro.Rmd</r:source><r:filename>intro.html</r:filename><r:title>An introduction to probabilistic-statistical variate analysis</r:title><r:created>2026-06-23 16:22:41</r:created><r:modified>2026-07-01 08:30:23</r:modified></r:article><r:article><r:source>mutualinfo.Rmd</r:source><r:filename>mutualinfo.html</r:filename><r:title>Associations among variates and mutual information</r:title><r:created>2025-07-30 08:28:22</r:created><r:modified>2026-07-01 05:20:16</r:modified></r:article></item><item><title>[pglpm] Pinference 0.2.6</title><author>pgl@portamana.org (PierGianLuca Porta Mana)</author><description>Implementation of T. Hailperin's procedure to calculate
lower and upper bounds of the probability for a
propositional-logic expression, given equality and inequality
constraints on the probabilities for other expressions.
Truth-valuation is included as a special case. Applications
range from decision-making and probabilistic reasoning, to
pedagogical for probability and logic courses. For more details
see T. Hailperin (1965) &lt;doi:10.1080/00029890.1965.11970533&gt;,
T. Hailperin (1996) &quot;Sentential Probability Logic&quot;
ISBN:0-934223-45-9, and package documentation. Requires the
'lpSolve' package.</description><link>https://github.com/r-universe/pglpm/actions/runs/28157535601</link><pubDate>Thu, 25 Jun 2026 06:33:16 GMT</pubDate><r:package>Pinference</r:package><r:version>0.2.6</r:version><r:status>success</r:status><r:repository>https://pglpm.r-universe.dev</r:repository><r:upstream>https://github.com/pglpm/pinference</r:upstream><r:article><r:source>inferP.Rmd</r:source><r:filename>inferP.html</r:filename><r:title>Probability bounds of logical expressions</r:title><r:created>2025-09-21 10:48:38</r:created><r:modified>2025-11-08 21:54:50</r:modified></r:article></item></channel></rss>