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pglpmVersion 0.3.1 of *inferno*, the R package for Bayesian nonparametric inference, is out!<<a href="https://pglpm.github.io/inferno/" rel="nofollow noopener" translate="no" target="_blank">https://pglpm.github.io/inferno/</a>>This version brings the following improvement and new functions:- Possibility of calculating the posterior probability of value ranges, such as Pr(Y ≤ y), besides of point values such as Pr(Y = y). Also for subpopulations. - New function to generate posterior samples for any set of variates. Also for subpopulations - Improved calculation of mutual information between variates.I'd like to remind that this package is especially suited to researchers with a frequentist background who'd like to try out Bayesian nonparametrics. The introductory vignette <<a href="https://pglpm.github.io/inferno/articles/inferno_start.html" rel="nofollow noopener" translate="no" target="_blank">https://pglpm.github.io/inferno/articles/inferno_start.html</a>> provides a simple and intuitive guide to the ideas, functions, and calculations, with a concrete example. The package provides many useful tools and functions for subgroup/subpopulation studies.The package is also suited to Bayesian researchers who'd like to do nonparametric analysis without worrying to much about the Monte Carlo coding and calculations that it often involves.Feedback and questions much appreciated!<a href="https://c.im/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#rstats</a> <a href="https://c.im/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#bayesian</a> <a href="https://c.im/tags/bayes" class="mention hashtag" rel="nofollow noopener" target="_blank">#bayes</a> <a href="https://c.im/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#datascience</a> <a href="https://c.im/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#statistics</a> <a href="https://c.im/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#MachineLearning</a>

Dr Mircea Zloteanu ☀️ 🌊🌴<a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#statstab</a> #398 Eta^2 for bayesian models {effectsize}Thoughts: Great resource, but scroll to "Eta Squared from Posterior Predictive Distribution"<a href="https://mastodon.social/tags/effectsize" class="mention hashtag" rel="nofollow noopener" target="_blank">#effectsize</a> <a href="https://mastodon.social/tags/eta2" class="mention hashtag" rel="nofollow noopener" target="_blank">#eta2</a> <a href="https://mastodon.social/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#bayesian</a> <a href="https://mastodon.social/tags/brms" class="mention hashtag" rel="nofollow noopener" target="_blank">#brms</a> <a href="https://mastodon.social/tags/r" class="mention hashtag" rel="nofollow noopener" target="_blank">#r</a><a href="https://easystats.github.io/effectsize/reference/eta_squared.html#eta-squared-from-posterior-predictive-distribution" rel="nofollow noopener" translate="no" target="_blank">https://easystats.github.io/effectsize/reference/eta_squared.html#eta-squared-from-posterior-predictive-distribution</a>

Ecology & Evolution of Health🗞️ Congrats to <a href="https://ecoevo.social/@OlivierSupplisson" class="u-url mention" rel="nofollow noopener" target="_blank">@OlivierSupplisson</a> from the team for his impressive analysis of massive <a href="https://mastodon.social/tags/HPV" class="mention hashtag" rel="nofollow noopener" target="_blank">#HPV</a> data (362,963 tests performed in France from 2020 to 2023) published in <a href="https://mastodon.social/tags/Eurosurveillance" class="mention hashtag" rel="nofollow noopener" target="_blank">#Eurosurveillance</a>. 🔎 This is analysis at the <a href="https://mastodon.social/tags/postcode" class="mention hashtag" rel="nofollow noopener" target="_blank">#postcode</a> level (!) was made using hierarchical <a href="https://mastodon.social/tags/Bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#Bayesian</a> models.It has implications for <a href="https://mastodon.social/tags/screening" class="mention hashtag" rel="nofollow noopener" target="_blank">#screening</a> as he shows a risk of systematic inflation of high-risk HPV infection prevalence.Maps also show increased prevalence for <a href="https://mastodon.social/tags/HPV16" class="mention hashtag" rel="nofollow noopener" target="_blank">#HPV16</a> and <a href="https://mastodon.social/tags/HPV18" class="mention hashtag" rel="nofollow noopener" target="_blank">#HPV18</a> in some areas<a href="https://doi.org/10.2807/1560-7917.ES.2025.30.28.2400689" rel="nofollow noopener" translate="no" target="_blank">https://doi.org/10.2807/1560-7917.ES.2025.30.28.2400689</a>

pglpmDear R community, I'd like to poll your opinions and ideas about the arguments of a possible R function:Suppose you're working with the variates of some population; for instance the variates `species`, `island`, `bill_len`, `bill_dep`, `body_mass`, etc. of the `penguins` dataset <<a href="https://cran.r-project.org/package=basepenguins" rel="nofollow noopener" translate="no" target="_blank">https://cran.r-project.org/package=basepenguins</a>>.Suppose there's a package that allows you to calculate conditional probabilities of single or joint variates; for examplePr( bill_len > 40, species = 'Adelie'  |  bill_dep < 16, body_mass = 4200)and note in particular that this probability refers to intervals/tails ("bill_len > 40") as well as to point-values ("body_mass = 4200").In fact the crucial point here is that with this function you can inquiry about the probability of a point value, "=", or about a cumulative probability, ">" or "<", or mixtures thereof, as you please.Now what would be the "best" way to input this kind of choice as an argument to the function? Let's say you have the following two input ways:**A: indicate the request of a cumulative probability in the variate name:**``` Pr( Y = list('bill_len>' = 40, species = 'Adelie'), X = list('bill_dep<' = 16, body_mass = 4200) ) ```**B: indicate the request of a cumulative probability in a separate function argument:**``` Pr( Y = list(bill_len = 40, species = 'Adelie'), X = list(bill_dep = 16, body_mass = 4200), tails = list(bill_len = '>', bill_dep = '<') # or +1, -1 instead of '>', '<'? ) ```Any other ideas? Feel free to comment :) See <<a href="https://pglpm.github.io/inferno/reference/Pr.html" rel="nofollow noopener" translate="no" target="_blank">https://pglpm.github.io/inferno/reference/Pr.html</a>> for a clearer idea about such a function.Thank you so much for your help!<a href="https://c.im/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#rstats</a> <a href="https://c.im/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#bayesian</a>

Dr Mircea Zloteanu ☀️ 🌊🌴<a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#statstab</a> #386 {bayestestR} Evaluating Evidence and Making Decisions using Bayesian Statistics by <a href="https://scicomm.xyz/@mattansb" class="u-url mention" rel="nofollow noopener" target="_blank">@mattansb</a> Thoughts: Want to start using Bayesian stats? Here is a quick but comprehensive guide in <a href="https://mastodon.social/tags/R" class="mention hashtag" rel="nofollow noopener" target="_blank">#R</a><a href="https://mastodon.social/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#bayesian</a> <a href="https://mastodon.social/tags/bayes" class="mention hashtag" rel="nofollow noopener" target="_blank">#bayes</a> <a href="https://mastodon.social/tags/mcmc" class="mention hashtag" rel="nofollow noopener" target="_blank">#mcmc</a> <a href="https://mastodon.social/tags/easystats" class="mention hashtag" rel="nofollow noopener" target="_blank">#easystats</a> <a href="https://mastodon.social/tags/guide" class="mention hashtag" rel="nofollow noopener" target="_blank">#guide</a><a href="https://mattansb.github.io/bayesian-evidence/" rel="nofollow noopener" translate="no" target="_blank">https://mattansb.github.io/bayesian-evidence/</a>

George E. 🇺🇸♥🇺🇦🇵🇸🏳️‍🌈🏳️‍⚧️The fact that there's no <a href="https://bofh.social/tags/algorithm" rel="nofollow noopener" target="_blank">#algorithm</a> is what's great about the <a href="https://bofh.social/tags/Fediverse" rel="nofollow noopener" target="_blank">#Fediverse</a>. Still doesn't mean platforms like <a href="https://bofh.social/tags/Sharkey" rel="nofollow noopener" target="_blank">#Sharkey</a> couldn't benefit from <a href="https://bofh.social/tags/Bayesian" rel="nofollow noopener" target="_blank">#Bayesian</a> filtering for our timelines! Let me up-vote or down-vote (like *cough* <a href="https://bofh.social/tags/reddit" rel="nofollow noopener" target="_blank">#reddit</a> *cough*) a post and filter out all the posts that I don't want to see!

Martin ModrákNew on the blog: Using Bayesian tools to be a better frequentist Turns out that for negative binomial regression with small samples, standard frequentist tools fail to achieve their stated goals. Bayesian computation ends up providing better frequentist guarantees. Not sure this is a general phenomenon, just a specific example.<a href="https://www.martinmodrak.cz/2025/07/09/using-bayesian-tools-to-be-a-better-frequentist/" rel="nofollow noopener" translate="no" target="_blank">https://www.martinmodrak.cz/2025/07/09/using-bayesian-tools-to-be-a-better-frequentist/</a><a href="https://bayes.club/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#rstats</a> <a href="https://bayes.club/tags/Bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#Bayesian</a> <a href="https://bayes.club/tags/brms" class="mention hashtag" rel="nofollow noopener" target="_blank">#brms</a> <a href="https://bayes.club/tags/stan" class="mention hashtag" rel="nofollow noopener" target="_blank">#stan</a>

Dr Mircea Zloteanu ☀️ 🌊🌴<a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#statstab</a> #382 The JASP Guidelines for Conducting and Reporting a Bayesian AnalysisThoughts: <a href="https://fosstodon.org/@JASPStats" class="u-url mention" rel="nofollow noopener" target="_blank">@JASPStats</a> is often people's first attempt at Bayesian statistics. But proper inference and reporting is crucial.<a href="https://mastodon.social/tags/JASP" class="mention hashtag" rel="nofollow noopener" target="_blank">#JASP</a> <a href="https://mastodon.social/tags/Bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#Bayesian</a> <a href="https://mastodon.social/tags/BayesFactor" class="mention hashtag" rel="nofollow noopener" target="_blank">#BayesFactor</a> <a href="https://mastodon.social/tags/guide" class="mention hashtag" rel="nofollow noopener" target="_blank">#guide</a> <a href="https://mastodon.social/tags/tutorial" class="mention hashtag" rel="nofollow noopener" target="_blank">#tutorial</a><a href="https://link.springer.com/article/10.3758/s13423-020-01798-5" rel="nofollow noopener" translate="no" target="_blank">https://link.springer.com/article/10.3758/s13423-020-01798-5</a>

Dr Mircea Zloteanu ☀️ 🌊🌴<a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#statstab</a> #377 To adjust, or not to adjust, for multiple comparisonsThoughts: Not all-encompassing, but it does cover some relevant notions about multiplicity adjustments.<a href="https://mastodon.social/tags/FDR" class="mention hashtag" rel="nofollow noopener" target="_blank">#FDR</a> <a href="https://mastodon.social/tags/FWER" class="mention hashtag" rel="nofollow noopener" target="_blank">#FWER</a> <a href="https://mastodon.social/tags/typeI" class="mention hashtag" rel="nofollow noopener" target="_blank">#typeI</a> <a href="https://mastodon.social/tags/error" class="mention hashtag" rel="nofollow noopener" target="_blank">#error</a> <a href="https://mastodon.social/tags/bonferroni" class="mention hashtag" rel="nofollow noopener" target="_blank">#bonferroni</a> <a href="https://mastodon.social/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#bayesian</a> <a href="https://mastodon.social/tags/multiplecomparisons" class="mention hashtag" rel="nofollow noopener" target="_blank">#multiplecomparisons</a><a href="https://www.jclinepi.com/article/S0895-4356%2825%2900021-6/fulltext" rel="nofollow noopener" translate="no" target="_blank">https://www.jclinepi.com/article/S0895-4356%2825%2900021-6/fulltext</a>

Peter HenryA monument to the triumph of vanity over safety? <a href="https://mastodonapp.uk/tags/Bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#Bayesian</a>

Daniel Hoffmann🌻Gene expression is tuned by <a href="https://mathstodon.xyz/tags/epigenetic" class="mention hashtag" rel="nofollow noopener" target="_blank">#epigenetic</a> changes, which explains why, say, liver and skin cells have the same genome but are otherwise different. Here we introduce a <a href="https://mathstodon.xyz/tags/Bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#Bayesian</a> model for the analysis of epigenetic changes during development. <a href="https://epigeneticsandchromatin.biomedcentral.com/articles/10.1186/s13072-025-00594-6" rel="nofollow noopener" translate="no" target="_blank">https://epigeneticsandchromatin.biomedcentral.com/articles/10.1186/s13072-025-00594-6</a>

nemo™ 🇺🇦Die Luxusjacht "Bayesian" ist aus dem Meer geborgen! 🚢🌊 Nach langer Zeit wurde das spektakuläre Wrack erfolgreich geborgen. Mehr dazu im Artikel: <a href="https://www.n-tv.de/panorama/Luxusjacht-Bayesian-endlich-aus-dem-Meer-geborgen-article25849786.html" rel="nofollow noopener" translate="no" target="_blank">https://www.n-tv.de/panorama/Luxusjacht-Bayesian-endlich-aus-dem-Meer-geborgen-article25849786.html</a> <a href="https://mas.to/tags/Luxusjacht" class="mention hashtag" rel="nofollow noopener" target="_blank">#Luxusjacht</a> <a href="https://mas.to/tags/Bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#Bayesian</a> <a href="https://mas.to/tags/Bergung" class="mention hashtag" rel="nofollow noopener" target="_blank">#Bergung</a> <a href="https://mas.to/tags/News" class="mention hashtag" rel="nofollow noopener" target="_blank">#News</a> <a href="https://mas.to/tags/newz" class="mention hashtag" rel="nofollow noopener" target="_blank">#newz</a>

MediaFaro News DigestSunken British superyacht Bayesian is raised from the seabed.A superyacht that sank off the coast of the Italian island of Sicily last year has been raised from the seabed by a specialist salvage team.Seven of the 22 people on board died in the sinking, including the vessel's owner, British tech tycoon Mike Lynch and his 18-year-old daughter.The cause of the sinking is still under investigation.<a href="https://mediafaro.org/article/20250620-sunken-british-superyacht-bayesian-is-raised-from-the-seabed?mf_channel=mastodon&action=forward" rel="nofollow noopener" translate="no" target="_blank">https://mediafaro.org/article/20250620-sunken-british-superyacht-bayesian-is-raised-from-the-seabed?mf_channel=mastodon&action=forward</a><a href="https://mastodon.mediafaro.org/tags/Italy" class="mention hashtag" rel="nofollow noopener" target="_blank">#Italy</a> <a href="https://mastodon.mediafaro.org/tags/UK" class="mention hashtag" rel="nofollow noopener" target="_blank">#UK</a> <a href="https://mastodon.mediafaro.org/tags/Bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#Bayesian</a> <a href="https://mastodon.mediafaro.org/tags/MikeLynch" class="mention hashtag" rel="nofollow noopener" target="_blank">#MikeLynch</a>

tagesschauVor Sizilien: Luxusjacht "Bayesian" wird geborgenDie Bergung des 56 Meter langen Segelschiffes hatte sich mehrfach verzögert. Nun ist es an der Oberfläche. Bei ihrem Untergang starben der britische Milliardär Mike Lynch und sechs weitere Insassen.➡️ <a href="https://www.tagesschau.de/ausland/europa/bayesian-bergung-102.html?at_medium=mastodon&at_campaign=tagesschau.de" rel="nofollow noopener" translate="no" target="_blank">https://www.tagesschau.de/ausland/europa/bayesian-bergung-102.html?at_medium=mastodon&at_campaign=tagesschau.de</a><a href="https://ard.social/tags/Bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#Bayesian</a> <a href="https://ard.social/tags/Schiffsungl%C3%BCck" class="mention hashtag" rel="nofollow noopener" target="_blank">#Schiffsunglück</a>

pglpmInterested in trying out *Bayesian nonparametrics* for your statistical research?I'd be very grateful if people tried out this R package for Bayesian nonparametric population inference, called "inferno" :<<a href="https://pglpm.github.io/inferno/" rel="nofollow noopener" translate="no" target="_blank">https://pglpm.github.io/inferno/</a>>It is especially addressed to clinical and medical researchers, and allows for thorough statistical studies of subpopulations or subgroups.Installation instructions are here: <<a href="https://pglpm.github.io/inferno/index.html#installation" rel="nofollow noopener" translate="no" target="_blank">https://pglpm.github.io/inferno/index.html#installation</a>>.A step-by-step tutorial, guiding you through an example analysis of a simple dataset, is here: <<a href="https://pglpm.github.io/inferno/articles/vignette_start.html" rel="nofollow noopener" translate="no" target="_blank">https://pglpm.github.io/inferno/articles/vignette_start.html</a>>.The package has already been tested and used in concrete research about Alzheimer's Disease, Parkinson's Disease, drug discovery, and applications to machine learning.Feedback is very welcome. If you find the package useful, feel free to advertise it a little :)<a href="https://c.im/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#rstats</a> <a href="https://c.im/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#bayesian</a> <a href="https://c.im/tags/bayes" class="mention hashtag" rel="nofollow noopener" target="_blank">#bayes</a> <a href="https://c.im/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#statistics</a> <a href="https://c.im/tags/medicine" class="mention hashtag" rel="nofollow noopener" target="_blank">#medicine</a> <a href="https://c.im/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#datascience</a>

Daniel Hoffmann🌻<a href="https://mathstodon.xyz/tags/Bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#Bayesian</a> analysis simplified (<a href="https://mathstodon.xyz/tags/BAYAS" class="mention hashtag" rel="nofollow noopener" target="_blank">#BAYAS</a>): our paper is out! We hope that many biologists & other users will find BAYAS helpful for experimental planning & data analysis. No programming or installation required. Will hopefully lead to reduction of lab animal numbers. <a href="https://mathstodon.xyz/tags/3R" class="mention hashtag" rel="nofollow noopener" target="_blank">#3R</a> <a href="https://mathstodon.xyz/tags/bayes" class="mention hashtag" rel="nofollow noopener" target="_blank">#bayes</a> <a href="https://doi.org/10.1093/bioinformatics/btaf276" rel="nofollow noopener" translate="no" target="_blank">https://doi.org/10.1093/bioinformatics/btaf276</a>

Dr Mircea Zloteanu ☀️ 🌊🌴<a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#statstab</a> #360 Bayes Factor Design Analysis {bfda}Thoughts: Sample size planning is confusing at first with Bayesian. But BFDA is the quick answer.<a href="https://mastodon.social/tags/bfda" class="mention hashtag" rel="nofollow noopener" target="_blank">#bfda</a> <a href="https://mastodon.social/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#bayesian</a> <a href="https://mastodon.social/tags/bayesfactor" class="mention hashtag" rel="nofollow noopener" target="_blank">#bayesfactor</a> <a href="https://mastodon.social/tags/samplesize" class="mention hashtag" rel="nofollow noopener" target="_blank">#samplesize</a> <a href="https://mastodon.social/tags/power" class="mention hashtag" rel="nofollow noopener" target="_blank">#power</a> <a href="https://mastodon.social/tags/errorrate" class="mention hashtag" rel="nofollow noopener" target="_blank">#errorrate</a><a href="https://shinyapps.org/apps/BFDA/" rel="nofollow noopener" translate="no" target="_blank">https://shinyapps.org/apps/BFDA/</a>

Dr Mircea Zloteanu ☀️ 🌊🌴<a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#statstab</a> #357 Uncertainty Estimation with Conformal PredictionThoughts: Haven't parsed this properly but maybe be an interesting discussion point. How best to quantify uncertainty?<a href="https://mastodon.social/tags/conformalprediction" class="mention hashtag" rel="nofollow noopener" target="_blank">#conformalprediction</a> <a href="https://mastodon.social/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#bayesian</a> <a href="https://mastodon.social/tags/confidenceintervals" class="mention hashtag" rel="nofollow noopener" target="_blank">#confidenceintervals</a> <a href="https://mastodon.social/tags/uncertainty" class="mention hashtag" rel="nofollow noopener" target="_blank">#uncertainty</a><a href="https://m-clark.github.io/posts/2025-06-01-conformal/" rel="nofollow noopener" translate="no" target="_blank">https://m-clark.github.io/posts/2025-06-01-conformal/</a>

PLOS BiologyRewarding animals to accurately report their subjective <a href="https://fediscience.org/tags/percept" class="mention hashtag" rel="nofollow noopener" target="_blank">#percept</a> is challenging. This study formalizes this problem and overcomes it with a <a href="https://fediscience.org/tags/Bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#Bayesian</a> method for estimating an animal’s subjective percept in real time during the experiment <a href="https://fediscience.org/@PLOSBiology" class="u-url mention" rel="nofollow noopener" target="_blank">@PLOSBiology</a> <a href="https://plos.io/3HaxiuB" rel="nofollow noopener" translate="no" target="_blank">https://plos.io/3HaxiuB</a>

Dr Mircea Zloteanu ☀️ 🌊🌴<a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#statstab</a> #350 Communicating causal effect heterogeneity By <a href="https://bayes.club/@matti" class="u-url mention" rel="nofollow noopener" target="_blank">@matti</a>Thoughts: Cool guide on properly communicating uncertainty in effects. <a href="https://mastodon.social/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#bayesian</a> <a href="https://mastodon.social/tags/uncertainty" class="mention hashtag" rel="nofollow noopener" target="_blank">#uncertainty</a> <a href="https://mastodon.social/tags/ggplot" class="mention hashtag" rel="nofollow noopener" target="_blank">#ggplot</a> <a href="https://mastodon.social/tags/r" class="mention hashtag" rel="nofollow noopener" target="_blank">#r</a> <a href="https://mastodon.social/tags/brms" class="mention hashtag" rel="nofollow noopener" target="_blank">#brms</a> <a href="https://mastodon.social/tags/tidybayes" class="mention hashtag" rel="nofollow noopener" target="_blank">#tidybayes</a> <a href="https://mastodon.social/tags/heterogeneity" class="mention hashtag" rel="nofollow noopener" target="_blank">#heterogeneity</a><a href="https://vuorre.com/heterogeneity-uncertainty/" rel="nofollow noopener" translate="no" target="_blank">https://vuorre.com/heterogeneity-uncertainty/</a>

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