R Weekly 2025-W44 Mapping Antarctica, Mambo No. 5, plant communities dirichlet

27 Oct 2025

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This week’s release was curated by Ryo Nakagawara & Batool Almarzouq, with help from the R Weekly team members and contributors.

Highlight

• Mapping Antarctica

• Can you predict when Lou Bega’s greatest hit, Mambo No. 5, was released based on the names of all the women mentioned in the song?

• Compositional modeling of plant communities with Dirichlet regression

Insights

• Futures: Interrupts, Crashes, and Retries

• Can you predict when Lou Bega’s greatest hit, Mambo No. 5, was released based on the names of all the women mentioned in the song?

• Cycles in Lynx Numbers

• Reducing the Backlog of Open Bug Reports

• A pathetic tale of searching for an R function on Google

R in the Real World

• Inequality and homicide, within-country and between country by @ellis2013nz

R in Organizations

• Recognition Beyond Blog Post Authors

R in Academia

• Two New Preprints on Multilevel Hidden Markov Models

• Tutorial Paper on Movement Tracking of Psychological Processes Using mousestrap

• Dataset and R codes for “Elevational Patterns of Plant Species Richness: Insights from Western Himalayas”

Tutorials

• Extracting location from text with Gemini

• Developing a Python package when coming from R: experience feedback

• Integrating Python Forecasting with R’s Tidyverse

• Be Mindful of the Time

• Speeding up UMAP plots for single cell gene expression analysis

• Compositional modeling of plant communities with Dirichlet regression

• Mapping Antarctica

Resources

• New, work-in-progress book on spatial data visualization in R

New Packages

📦 Keep up to date wtih CRANberries 📦

CRAN

• partialling.out 0.2.0: Partial residuals from linear and fixed effects models
• {galaxias} 0.1.1: Build Darwin Core Archives in R
• {rdav} 0.1.3: Simple ‘WebDAV’ Client
• {sumup} 1.0.0: Utilizing Automated Text Analysis to Support Interpretation of Narrative Feedback
• {poseticDataAnalysis} 0.1.0: Posetic Data Analysis
• {CompositionalSR} 1.0: Spatial Regression Models with Compositional Data
• {tind} 0.2: A Common Representation of Time Indices of Different Types
• {OmopOnSpark} 0.1.0: Using a Common Data Model on ‘Spark’
• {ci} 0.0.1: Confidence Intervals for Education
• {bayesQRsurvey} 0.1.4: Bayesian Quantile Regression Models for Complex Survey Data Analysis
• {scf} 1.0.4: Analyzing the Survey of Consumer Finances
• {NeEDS4BigData} 1.0.1: New Experimental Design Based Subsampling Methods for Big Data
• {mcmodule} 1.1.0: Modular Monte Carlo Risk Analysis
• {CFM} 0.6.0: Analyzing Censored Factor Models
• {BRPL} 1.0.2: Methods for Bivariate Poverty Line Calculations
• {alpmixBayes} 0.1.0: Bayesian Estimation for Alpha-Mixture Survival Models
• {GHRmodel} 0.1.0: Bayesian Hierarchical Modelling of Spatio-Temporal Health Data
• {testthatdocs} 1.0.23: Automated and Idempotent Unit Tests Documentation for Reproducible Quality Assurance
• {LLMing} 1.0.0: Large Language Model (LLM) Tools for Psychological Text Analysis
• {FracFixR} 1.0.0: Compositional Statistical Framework for RNA Fractionation
• {EFM} 0.5.0: Elliptical Factor Models
• {AgeTopicModels} 0.1.0: Inferring Age-Dependent Disease Topic from Diagnosis Data
• {Rfuzzycoco} 0.1.0: Provides an R Interface to the ‘FuzzyCoCo’ C++ Library and Extends It
• {paisaje} 0.1.1: Spatial and Environmental Data Tools for Landscape Ecology
• {evanverse} 0.3.7: Utility Functions for Data Analysis and Visualization
• {tteICE} 1.0.1: Treatment Effect Estimation for Time-to-Event Data with Intercurrent Events
• {SimplicialComplex} 0.1.0: Topological Data Analysis: Simplicial Complex
• {rosario} 0.1.0: A Null Model Algorithm to Analyze Cyclical Data in Ecology
• {faunabr} 1.0.0: Explore Catálogo Taxônomico da Fauna do Brasil Database
• {epmfd} 1.1.1: Exploratory and Person/Item Misfit Diagnostics for Polytomous Data
• {CortSineScore} 0.1.0: Compute Cortisol Sine Score (CSS) for Diurnal Cortisol Analysis
• {compIndexBuilder} 1.0.0: Composite Index Builder & Analytics ‘shiny’ App
• {bigPCAcpp} 0.9.0: Principal Component Analysis for ‘bigmemory’ Matrices
• {funbootband} 0.2.0: Simultaneous Prediction and Confidence Bands for Time Series
• {arete} 0.1: Automated REtrieval from TExt
• {RPIV} 1.0.0: Residual Prediction Test for Well-Specification of Instrumental Variable Models
• {lightsf} 0.1.0: A Curated Collection of Georeferenced and Spatial Datasets

Updated Packages

🔍 Search on R-universe 🔍

• progressr 0.17.0: An Inclusive, Unifying API for Progress Updates - diffify
• ML quacks: Combining duckdb and mlpack
• qlcal 0.0.17 on CRAN: Regular Update
• RcppSpdlog 0.0.23 on CRAN: New Upstream
• {vntrs} 0.2.0: Variable Neighborhood Trust Region Search - diffify
• {StreamCatTools} 0.9.1: ‘StreamCatTools’ - diffify
• {hdtg} 0.2.2: Generate Samples from Multivariate Truncated Normal Distributions - diffify
• {bskyr} 0.4.0: Interact with ‘Bluesky’ Social - diffify
• {mintyr} 0.1.2: Streamlined Data Processing Tools for Genomic Selection - diffify
• {reticulate} 1.44.0: Interface to ‘Python’ - diffify
• {ggsci} 4.1.0: Scientific Journal and Sci-Fi Themed Color Palettes for ‘ggplot2’ - diffify
• {rtestim} 1.0.1: Estimate the Effective Reproductive Number with Trend Filtering - diffify
• {tinytiger} 0.0.11: Lightweight Interface to TIGER/Line Shapefiles - diffify
• {tidypopgen} 0.4.0: Tidy Population Genetics - diffify
• {pracma} 2.4.6: Practical Numerical Math Functions - diffify
• {tikatuwq} 0.7.2: Water Quality Assessment and Environmental Compliance in Brazil - diffify
• {googleLanguageR} 0.3.1.1: Call Google’s ‘Natural Language’, ‘Cloud Translation’, ‘Cloud Speech’, and ‘Cloud Text-to-Speech’ APIs - diffify
• {BAwiR} 1.4: Analysis of Basketball Data - diffify
• {warbleR} 1.1.37: Streamline Bioacoustic Analysis - diffify
• {unitizer} 1.4.23: Interactive R Unit Tests - diffify
• {rfars} 2.0.2: Download and Analyze Crash Data - diffify
• {promises} 1.4.0: Abstractions for Promise-Based Asynchronous Programming - diffify
• {VSURF} 1.2.1: Variable Selection Using Random Forests - diffify
• {svglite} 2.2.2: An ‘SVG’ Graphics Device - diffify
• {clifford} 1.1-2: Arbitrary Dimensional Clifford Algebras - diffify
• {MLE} 1.6: Maximum Likelihood Estimation of Various Univariate and Multivariate Distributions - diffify
• {henna} 0.3.4: A Versatile Visualization Suite - diffify

Videos and Podcasts

• Listen to the R-Weekly Highlights Podcast

• [ R demo

  ANOVA (One-Way)

  Fisher’s, Welch’s, Bayesian, Robust

  Conduct, visualise, interpret + 😉 ](https://www.youtube.com/watch?v=JDGtLG0Tceo)

• What Makes a Good Data Visualization?

• Explore #TidyTuesday literary prizes with Positron’s Data Explorer

• Regression on Order Statistics (ROS) Use ros (NADA2) With (In) R Software

Gist & Cookbook

• Positron: data explorer exports dplyr code

R Project Updates

Updates from R Core:

Updates from the R Contribution Working Group (RCWG):

• Announcing R Dev Day @ New Zealand 2025: Auckland University or remote (Americas / Asia-Pacific), Tue 16 - Wed 17 (NZST). Join us to work on contributions to the code, documentation and translations in base R. Free and open registration, deadline Sunday 7 December.

Call for Participation

Post by @[email protected]

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Upcoming Events in 3 Months

Events in 3 Months:

• A list of R conferences and meetings

• This week’s local R-User and applied stats events

• Weekly R Workshops for Ukraine

• Graceful Internet Packages: Thursday, 06 November 2025 15:00 UTC

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Quotes of the Week

A visualisation of forest canopy height in Shiretoko National Park, Hokkaido, two ways. Which do you prefer? I can never decide. What I can say is that Shiretoko is beautiful. And windy. And the sika are unafraid. Also, hot waterfalls.#rayshader adventures, an #rstats tale pic.twitter.com/O16SBsOzla

— terence (@researchremora) October 24, 2025

One of my favorite aspects of the R community is the hex sticker culture. To this day one of my proudest #rstats contributions was artwork for two: purrr and forcats

[image or embed]

— We Are R-Ladies (@weare.rladies.org) October 22, 2025 at 4:26 AM

I'm excited to share a new version of the Air formatter for #rstats, with support for tabular formatting! Super useful with `tibble::tribble()` calls or `data.table::fcase()`. It's currently experimental as we're looking to get feedback on the feature, so please let us know what you think.

[image or embed]

— Lionel Henry (@lionelhenry.bsky.social) October 21, 2025 at 7:34 PM