The R Validation Hub’s risk-based framework is how many regulated teams trust R packages. AI is starting to look useful in that workflow (reviewing maintenance signals at scale, drafting remediation tests, flagging version changes) and it also raises a harder question:

How should risk assessment treat packages or contributions that are themselves AI-generated or AI-assisted?

At R+AI 2026, Ileana Saenz (R Validation Hub / Appsilon) and Doug Kelkhoff (Chair, R Validation Hub / Domino) lead a live working session to dig into where AI can realistically help, where AI-assisted evaluation introduces new risks that need validation of their own, and what a minimal, practical pilot could look like. Session notes feed a community whitepaper.

Where Does AI Fit in R Package Risk-Based Assessment?: A Working Session Toward a Community Whitepaper

November 10 · 3:40 PM Eastern · Online workshop

If you work in R package validation, regulated environments, or applied AI tooling, this one’s built for you.

R+AI 2026 is the R Consortium’s conference on R and AI: November 10–11, fully online, with live Q&A and recordings.

See the full workshop info:

https://rconsortium.github.io/RplusAI_website/workshops.html#where-does-ai-fit

I am in the final 6 weeks of my seasonal botany technician job, and the conservation corps I work for allotted me ~$900 in professional development funds that I have yet to use. I took a Biostatistics course (all in Rstudio) at my university and continued to use the basic R skills I learned as I did undergrad research and after graduation when I moved into my seasonal job.

I want to use the professional development funds, but only if it will actually be valuable to me as I apply for other jobs in the botany/wildlife biology field. If anyone has experience as a hiring manager or similar role, would a certificate (such as Harvard edX Data Analysis for Life Sciences) add substantial value to my resume? Or is it just as valuable to work through free online resources (such as R for Data Science) and build a GitHub repository to display my skills to potential employers?

A little update on a project I shared here recently: termr is now available on CRAN!

It's a framework for building interactive terminal applications in R. Think data browsers, dashboards, SQL tools, or just nicer interfaces around existing R workflows.

It has keyboard navigation, widgets, layouts, reactive state, themes, and a headless testing API. There's also experimental support for SQLite/DBI workflows, which a few people here mentioned they'd find useful.

You can now install it directly:

install.packages("termr")

GitHub: https://github.com/menshikovs/termr

CRAN: https://CRAN.R-project.org/package=termr

Most of this release came from the feedback on my 0.1.1 post here a few months ago, so thank you to everyone who tried it and told me what was wrong

glyph 0.2.0

If something breaks, just let me know. (suggestions and critiques are welcome).

I am testing the correlation between the values of a variable at the tips of a phylogenetic tree (let's say the beak length) and the pathwise rate of the trait itself (i.e. a measure of the total changes in rate a species has experienced during the course of its evolution). To remove the effect of body size, I am working on the residual of the variable (i.e. the variable corrected for SVL). To test for the correlation, I performed a pgls with the pgls() function in lmtest (I am working in RStudio). I am encountering some issues with heteroskedasticity (see attached images; bptest significant).

I am also having problems with the normality of the residuals (see attached images; Shapiro test significant). On the other hand, I was reading from https://blog.phytools.org/2013/02/a-comment-on-distribution-of-residuals.html that, by accounting for residuals controlling for phylogeny you should solve this problem [Revell suggests something like lillie.test(chol(solve(vcv(tree)))%*%residuals(corr))]. Can anyone confirm? Am I misunderstanding?

Can anyone help with it?

Thanks a lot!

Hi everyone!

I'm a student from Pakistan, and I've been learning R through Harvard's CS50R course. I've worked through the problem sets, and now I'm wondering what I should do next to turn my learning into practical experience.

My goal is to work on small, real-world projects involving data analysis and visualization, and eventually earn some money using R.

I'd appreciate advice on a few things:

1. What should I learn or revise after CS50R?
2. Should I build more portfolio projects before looking for clients or internships?
3. Where can beginners find small R programming or data analysis projects?
4. Is it realistic to get paid work with CS50R knowledge, or should I learn additional tools first?
5. If you started with R as a beginner, what would you do differently?

I'm not expecting to become an expert overnight. I just want a practical roadmap to move from learning R to actually using it on real projects.

Thanks for any advice!

Fine-tuning needs hundreds of training examples, and most of us only have documents. dragonfarm turns one into the other on your own laptop, with RAG built in so answers stay grounded in your documents. Feedback welcome, especially documents that break it.

Write-up with measurements: https://dragonfarm.dev/articles/introducing-0-4-0.html

GitHub: https://github.com/tejas4patel/dragonfarm

Feedback welcome, especially documents that break it.

The full R+AI 2026 schedule is up, and it is a good one!

November 10–11. 100% online.

Two days of keynotes, talks, lightning talks, a workshop, and a tutorial on how the R community is putting AI to work.

A taste of the program:

• Why statistical thinking still matters when generative AI writes the code
• Agents in R, from rebuilding Claude Code in 50 lines to auditable human–AI workflows
• Private AI for sensitive data, and governance for agentic systems in production
• A workshop on where AI fits in risk-based R package assessment
• A tutorial on shipping R drug-discovery models from the lab onto an enterprise platform

See the full schedule and register now! https://rconsortium.github.io/RplusAI_website/program.html

You don’t have to be a C programmer to contribute to R.

R has been maintained for more than 30 years. Its future depends on more people being able to take part in that work.

In the R Consortium webinar Sustaining R, R Core members Simon Urbanek, Kurt Hornik, and Heather Turner answered a question that comes up often: how can an R user contribute to R itself?

The answer is broader than writing language internals. You can:

• Report a bug or propose a feature on Bugzilla or R-devel
• Improve documentation, translations, testing, or contributor infrastructure
• Join an R Dev Day or R Contributor Office Hour
• Build R from source and submit a patch

The new post covers how to get started, why R still uses Subversion and Bugzilla, and how R Core is thinking about AI-assisted contributions: useful as a tool, as long as a person understands the work and takes responsibility for it.

Read the guide: https://r-consortium.org/posts/how-do-i-contribute-to-r/

Released ieiprio, an R package to prioritize variants in inborn errors of immunity from VCF, Sanger traces or plain text. It ships 12 real ClinVar variants to practice on, offline. Guides and code below, feedback welcome.
https://gentlenmoron.github.io/ieiprio/

It's so odd for R to have no arithmetic assignment operators like +=, which this feature is so convenient to write. Python has them, Julia has them - the list still keeps coming up. I know you can write your own infix operator in R, enclosing them with %%:

`%+=%` <- function(e1, e2) {
  eval(substitute(e1 <- e1 + e2), envir = parent.frame())
}
a <- 2
a %+=% 7
a  # 9

But would be nice if R has them.

A pharmacometric fit can take minutes to weeks. nlmixr2save keeps that fit readable, and skips the rerun when nothing has changed.

A fit is the saved result of estimating a drug model against observed data, such as measured concentrations.

The nlmixr2 Working Group’s latest post introduces nlmixr2save 0.2.0, now on CRAN. saveFit() stores a fit as text: models as R source, tables as CSV, bundled in one zip. loadFit() brings back a working fit for plots, visual predictive checks, and the next model. Switch <- to := and a script or report loads from cache until the model, the data, or the estimation options change.

Matthew Fidler covers the same material on the nlmixr2save poster at ACoP 2026 (October 11–14, National Harbor).

A 7-minute walkthrough is linked from the post.

https://r-consortium.org/posts/nlmixr2save/

In my experience, Positron feels extremely laggy, slow, and riddled with bugs. Meanwhile, RStudio has been blazing fast, stable, and virtually trouble-free. I've rarely encountered issues with environment dependencies, package installations, or general usability.

It literally takes about five minutes to install R and RStudio and get straight to work. Everything just works, without having to spend time troubleshooting the IDE itself.

I really hope Posit continues actively developing and supporting RStudio rather than eventually forcing everyone to migrate to Positron

To me, RStudio is a much more polished, reliable, and efficient IDE for working with R. I genuinely don't see the appeal of moving to Positron when RStudio already does its job so well.

Rstudio should also support seamless Python integration, hate to use Positron just for Python

Hi,

Some people may remember that the author of this package posted last year about changing package ownership because he didn’t had time to resolve issues that eventually caused the package to be removed from CRAN.

I suggested a PR but since he didn’t respond in a while I decided to resubmit the package back to CRAN from my fork and maintain it at least for the time being.

I thought it’s a good idea to post here as the package seems quite popular.

If any users find issues or want to contribute the code is now maintained on my GitHub: https://github.com/novica/ParBayesianOptimization

What's new:

• Closer to iOS 27 Liquid Glass: a refraction lens at the glass edges (Chromium), tab indicators that slide between items, buttons that spring when pressed, and tab bars that shrink as you scroll
• Mobile ready: 44px tap targets, phone sidebars open as a full overlay, and no sideways scroll at iPhone widths
• Every live demo has a phone preview button on desktop
• shinychat apps pick up the glass look, shown off in a new chat demo (Harbor notes) that can reply live with Gemini
• shinyreact support, with React glass components that share the same theme
• The ?shinyglass help page now walks through the whole setup

Heads up that some visual defaults changed on purpose. The migrating vignette shows how to get the 0.4.0 look back.

install.packages("shinyglass")

Live demos (may take a few seconds to wake):

• Harbor notes chat
• Main demo
• Dashboard
• Inputs gallery
• Olympic medals

Docs: https://ericrayanderson.github.io/shinyglass/

CRAN: https://cran.r-project.org/package=shinyglass

I maintain tidycreel, which has just been accepted on CRAN as version 8.0.0.

Creel surveys use angler counts and interviews to estimate fishing effort, catch, and harvest. The analysis depends on how days, sites, and anglers were sampled. tidycreel provides a tidy, pipe-friendly interface for keeping that survey structure attached to the data, with the survey package supplying the design-based foundation.

The core workflow is to define a survey calendar and strata, attach count and interview data, check coverage and data quality, and estimate effort, rates, and totals with uncertainty. The distinction between completed access-point trips and incomplete roving interviews matters when choosing a rate estimator.

Install the CRAN release:

install.packages("tidycreel")

The source package is available now. Windows and macOS binaries are pending; I expect them within the next couple of days. Until then, use install.packages("tidycreel", type = "source") if a binary is unavailable. tidycreel itself has no compiled code, although source-built dependencies may need build tools.

For development changes ahead of CRAN:

install.packages("pak")  # If needed
pak::pak("chrischizinski/tidycreel")

In 8.0.0, aerial catch, harvest, and release totals are underestimated; use the GitHub development version for those totals until 8.0.1 reaches CRAN (release notes). Non-aerial designs are unaffected.

There are 24 package vignettes, a practical blog series, and the companion book Modern Creel Survey Analysis in R. The book remains a work in progress and develops the sampling and statistical reasoning behind the workflows.

I also wrote a short release post with installation instructions and links to these resources.

I would welcome feedback from people working with creel or other field survey data, especially on data structures and diagnostics. What sampling or data coverage problems are hardest to make visible in your current workflow?

Source and issue tracker.

I am more of a stats guy, so I don't do this every day, but I needed to describe some nonlinear relationships by category in a lightweight way.

It seems Shap contributions are a valid and popular approach. Not the Shap values (a vector), but the contributions by variable (a matrix or data frame).

Here's my experience trying to do this in R.

1. I first tried {ranger}. ranger makes is easy to fit, predict, and get importances, but does not have any support for Shap contributions.

2. I tried to use {fastshap} in combination with ranger. fastshap is removed from CRAN! This is the package author's comment on the package GitHub. "fastshap is no longer available on CRAN due to CRAN’s stringent and ever-changing policies. It is now hosted on r-universe, which provides a reliable alternative for distributing R packages."

3. I tried to use {treeshap} instead. treeshap is on CRAN. treeshap needs to unify tree regression objects first from various packages (ranger, xgboost, etc.). treeshap::ranger.unify() crashed R 66% of the time! When it does succeed, treeshap::importance() crashed R 100% of the time!

4. I found that {xgboost} has support for Shap contributions within its own package. Good. I fit a model successfully. I calculate the Shap contributions by predict(model, newdata = x, predcontrib = TRUE) . This only returns the Shap values (vector; not matrix). Why? Is predcontrib not doing anything? I debug for a long time. I find that my fit model is an xgboost object, and there is another possible model object produced by the package of class xgb.Booster. The method I thought I was using was predict.xgb.Booster, but predict was actually dispatching to predict.xgboost, which does not have a predcontrib argument. I have to hack the generics by calling xgboost:::predict.xgb.Booster(). Finally, I feel like it worked.

Hi everyone!

As part of my Master's thesis in Statistics, I developed hdsbm, an R package for estimating and simulating discrete-time Dynamic Stochastic Block Models (DSBM).

The Problem & Approach

Temporal block models often suffer from combinatorial explosion and multi-modal likelihood landscapes, making standard local search heuristics prone to getting trapped in sub-optimal local extrema.

hdsbm addresses this by extending a hybrid Classification EM (CEM) approach to the dynamic setting: * Perturbed SVD Multi-Start: Couples deterministic spectral embeddings of the time-averaged network with controlled stochastic perturbations to thoroughly explore the likelihood surface. * C++ Backend: Uses RcppArmadillo to accelerate dyadic reallocation passes and combinatorial greedy sweeps. * Dual Decoding: Provides both pointwise greedy labels and global Viterbi trajectory decoding. * Model Selection: Includes an Integrated Completed Likelihood (ICL) criterion tailored for dynamic graphs.

Quick Example

```r library(hdsbm) data(toy_hdsbm)

Fit model with 5 perturbed spectral restarts

fit <- hdsbm_fit(toy_hdsbm$Y, k = 3, nrep = 5, seed = 123) print(fit)

Model selection via ICL

icl(fit) ```

• GitHub: https://github.com/Titolise/Hybrid-DSBMs

• Documentation & pkgdown: https://titolise.github.io/Hybrid-DSBMs/

I would love to hear your feedback on API ergonomics, potential edge cases, or ideas for interesting benchmark scenarios!