R Weekly 2026-W35 Visualization Creativity, Futurize, Tricky Async

24 Aug 2026

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This week’s release was curated by Eric Nantz, with help from the R Weekly team members and contributors.

Editor’s Note: This week’s issue also includes resources from the 2026-W34 curation window. Enjoy a super-sized issue of R Weekly!

Highlight

• Why creativity matters in data visualisation

• useR! 2026: Futurize - Tearing Down Parallelization Barriers in R with Transpilers

• Tricky Parts of Async Mirai Magic

Insights

• Deprecation messages for package data

• Reading notes on Code That Fits in Your Head by Mark Seemann

• Reading notes on The Programmer’s Brain by Felienne Hermans

• Why creativity matters in data visualisation

• Breaking the Python Barrier: Building a Pure R-Native DeepAR Engine with LibTorch

R in the Real World

• From Census Data to Demographic Analysis with ARcenso: A Reproducible Workflow in R

• A Summer, Explained with R

R in Organizations

• Decorator Pattern in R for Clinical Statistics

R in Academia

• Bayesian Thinking

• McNemar’s test in R

Tutorials

• Tricky Parts of Async Mirai Magic

• TidyTuesday 2026/32

• Using GitHub Actions to deploy to Posit Connect Cloud

• Learning how to extract parts of a string

• Creating self-contained R scripts for rendering Quarto documents using the knitr engine – courtesy of the new R package managers ir and uvr

Resources

• uvr: A uv-style package and project manager for R, written in Rust. One tool for the R version, a manifest and lockfile, and a per-project isolated library, installing from pre-built P3M binaries. Scripts can also declare dependencies in an inline header and run standalone with no project — the R analogue of PEP 723. Usable from R via a companion package.

• The Resource Project: Tell R How Much Memory You Need

• useR! 2026: Futurize - Tearing Down Parallelization Barriers in R with Transpilers

• Typst for Efficient Typesetting

New Packages

📦 Keep up to date wtih CRANberries 📦

CRAN

{fru} 1.0.0: A Blazing Fast Implementation of Random Forest

• {ratingtables} 0.2.0: Table-Driven Insurance Rating

• {rapsimng.decide.core} 0.1.0: Decision-Oriented Analysis Core for APSIM Next Generation Outputs

• {ProgVine} 0.1.0: Progressive Regularized Vine Copula for Masked Competing Risks

• {PINNProgCens} 0.1.0: Physics-Informed Neural Networks for Progressive Censoring

• {phontrast} 2.3.1: Contrast and Separation Metrics for Phonological Categories

• {palettecore} 0.4.1: Derive, Optimise and Audit a Scientific Colour Palette from One Seed Colour

• {NeutroBasicDesignsAnalysis} 0.0.1: Neutrosophic Analysis of Completely Randomized Designs and Randomized Complete Block Designs

• {ModToppLeone} 0.1.0: Modified Topp-Leone Distribution: Properties, Estimation, and Applications

• {MiCT} 2.0.0: Minimal Important Change and Threshold Estimation

• {IntegMultiReg} 0.1.0: Integrative Bayesian Multiple Regression for Multi-Platform Biomarkers

• {gofLorenz} 0.1.0: Goodness-of-Fit Tests for Location-Scale Distributions via Lorenz Curve

• {GLHD} 1.1-1: Grouped Latin Hypercube Designs with Controlled Correlations

• {exactGMH} 0.1.0: Exact and Permutation-Based Mantel Tests for Differential Item Functioning in Dichotomous and Polytomous Items

• {smartcor} 1.0.1: Automatic Correlation Method Selection Based on Variable Types

• {PLSsemEngine} 1.3.0: Transparent PLS-SEM Estimation for Composite-Based Reflective Models

• {metabodeconplus} 0.22.0: Deconvolution, Alignment and Model Fitting of 1d NMR Spectra

• {gp3bayes} 0.1.1: Contract-First Bayesian Workflows for Hierarchical Behavioural Data

• {FPScausal} 0.1.0: Functional Propensity Score for Causal Inference

• {FINN} 0.1.0: Forest Informed Neural Networks

• {expoquimR} 0.1.0: Qualitative and Quantitative Assessment of Occupational Chemical Exposure Risk

• {DropCens} 0.1.0: Distributionally Robust Progressive Type-II Censoring Inference

• {qvivid} 0.1.2: Fast Quantum Simulation and Vivid Visualizations

• {netsimhelpers} 0.1.0: Helper Functions for Simulation Studies in Network Psychometrics

• {gghotelling} 0.2.1: Hotelling’s T² Ellipses and Outlier Detection for ‘ggplot2’

• {FusionForests} 1.0.1: Bayesian Tree Ensembles for Data Fusion and Causal Inference

• {bscm} 1.0.1: Bayesian Synthetic Control Models

• {MuTATE} 0.1.0: Multi-Target Automated Tree Engine (MuTATE)

• {CoxAalenCR} 0.1.0: Additive-Multiplicative Cox-Aalen Subdistribution Hazard Model for Competing Risks

• {TieFreeCensor} 0.1.0: Algorithm for Generating Tie-Free Progressive Type-II Censored Samples

• {tabulergm} 0.1.0: Publication-Ready Tables and Summaries for Exponential-Family Random Graph Models

• {rankingQ} 0.2.0: Design-Based Methods for Ranking Questions

• {PowerXgammaRF} 1.0.0: Random Forest Regression with Power Xgamma Distribution Error Model

• {pHMC} 0.1.0: Proximal Hamiltonian Monte Carlo for Non-Smooth Bayesian Inference

• {openfhe.R} 1.5.1: R Interface to the ‘OpenFHE’ Fully Homomorphic Encryption Library

• {MTLRF} 1.0.0: Random Forest Regression with Modified Topp-Leone Error Model

• {investdatar} 0.1.5: Investment Data Access and Preparation Toolkit

• {GFT} 1.0.0: Generalized Fisher Transformation of Correlation Matrices

• {FITclust} 1.0.0: Fair Interpolated Transport for Group-Fair Clustering

• {citcdf} 1.1.0: Conditional Independence Testing with Cumulative Distribution Functions

• {bruno} 0.1.0: Predicting User-Defined Event Recurrence under Exchangeability

• {brfssdata} 0.1.0: Access CDC Behavioral Risk Factor Surveillance System Data

• {autotune} 0.1.0: Faster and more Efficient Lasso (than ‘glmnet’ and ‘scalreg’) with Data-Driven Tuning

• {AstraeaDB} 0.2.1: Client for the ‘AstraeaDB’ Graph Database

• {ArvindRF} 1.0.0: Random Forest Regression with Arvind Distribution Error Model

• {OneShotEM} 0.1.0: Efficient eM-Algorithm for One-Shot Device Data Analysis

• {AugBalWeight} 0.1.0: Augmented Balancing Weights as Linear Regression

• {ivdtools} 0.1.2: Statistical Tools for Evaluation of in Vitro Diagnostic Reagents

• {dgraphs} 0.1.0: Data-Derived Graph Construction Utilities

• {underdisp} 0.1.0: Diagnostics and Models for Underdispersed Count Data

• {weatherMRJD} 0.1.1: Weather Analysis and Markov Regime Switching Jump Diffusion Models

• {robustrcp} 0.1.0: Outlier-Robust Ratio-cum-Product Estimators of Finite Population Mean

• {MLMES} 0.1.2: Model-Based Effect Sizes for Multilevel Models

• {caverify} 0.1.2: Fast Strength-t Coverage Verification for Covering Arrays

• {themescopeR} 0.1.1: Social Representation Analysis via Semantic Network Mapping

• {scmix} 0.1.1: Bayesian Model-Based Clustering with Sparse Conditional Mixture Forecasting

• {ofemeantest} 1.0.0: On Farm Experimentation Mean Test

• {nwaa} 0.1.4: USGS NWAA Data Download

• {mditools} 0.1.0: Microdata Infrastructure Tools for Firm-Level Microdata Research

• {lineager} 0.1.1: Row-Level Data Provenance and Exclusion Tracking

• {kpp2019} 0.0.1: Kenya Population Projections 2019

• {grout} 0.1.0: Abstract Raster Tiling Schemes

• {alepe} 0.1.0: Access the Open Data API of the Legislative Assembly of Pernambuco

GitHub or Bitbucket or GitLab

• {uvr}: R interface to uvr — manage R versions, lockfiles and isolated project libraries from the R console, RStudio or Positron without dropping to a terminal.

Updated Packages

🔍 Search on R-universe 🔍

• nlmixr2 7.0

• themis 1.1.0

• RcppMsgPack 0.2.5 on CRAN: Minor Maintenance

• cuda.ml 0.4.0: GPU-accelerated machine learning from R

• {tm} 0.7-19: Text Mining Package - diffify

• {tm.plugin.mail} 0.3-2: Text Mining E-Mail Plug-in - diffify

• {lattice} 0.23-1: Trellis Graphics for R - diffify

• {fastICA} 1.2-8: FastICA Algorithms to Perform ICA and Projection Pursuit - diffify

• {KernSmooth} 2.23-27: Functions for Kernel Smoothing Supporting Wand & Jones (1995) - diffify

• {tseries} 0.10-63: Time Series Analysis and Computational Finance - diffify

• {RODBC} 1.3-26.2: ODBC Database Access - diffify

• {missknn} 1.1.2: Fast Masked K-Nearest Neighbor Imputation - diffify

• {mcstatsim} 0.5.1: Monte Carlo Statistical Simulation Tools Using a Functional Approach - diffify

• {SLGP} 1.1.0: Spatial Logistic Gaussian Process for Field Density Estimation - diffify

• {RCurl} 1.98-1.20: General Network (HTTP/FTP/…) Client Interface for R - diffify

• {pkgfilecache} 0.2.0: Download and Manage Optional Package Data - diffify

• {XML} 3.99-0.24: Tools for Parsing and Generating XML Within R and S-Plus - diffify

• {SuperLearner} 2.0-41: Super Learner Prediction - diffify

• {SSBtools} 1.8.8: Algorithms and Tools for Tabular Statistics and Hierarchical Computations - diffify

• {soma} 1.2.1: General-Purpose Optimisation with the Self-Organising Migrating Algorithm - diffify

• {safetensors} 0.3.0: Safetensors File Format - diffify

• {S7schema} 0.1.2: ‘S7’ Framework for Schema-Validated YAML Configuration - diffify

• {RProtoBuf} 0.4.28: R Interface to the ‘Protocol Buffers’ ‘API’ (Version 2 or 3) - diffify

• {QuickJSR} 1.11.0: Interface for the ‘QuickJS-NG’ Lightweight ‘JavaScript’ Engine - diffify

• {PatientProfiles} 1.6.1: Identify Characteristics of Patients in the OMOP Common Data Model - diffify

• {multcomp} 1.4-32: Simultaneous Inference in General Parametric Models - diffify

• {jsutils} 0.4.0: ‘JavaScript’ Utilities for ‘R’ - diffify

• {BMIselect} 1.0.9: Bayesian MI-LASSO for Variable Selection on Multiply-Imputed Datasets - diffify

• {RATest} 0.1.11: Randomization Tests - diffify

• {mcgf} 1.2.0: Markov Chain Gaussian Fields Simulation and Parameter Estimation - diffify

• {mapnhanespa} 0.2.0: Map Quantiles for Physical Activity from ‘NHANES’ - diffify

• {IsoplotRgui} 7.0: Web Interface to ‘IsoplotR’ - diffify

• {freegroup} 1.2-1-1: The Free Group - diffify

• {flightsbr} 1.2.0: Download Flight and Airport Data from Brazil - diffify

• {FinanceGraphs} 0.9.2: Flexible Graphs for Analysis of Financial Data and Time Series - diffify

• {familiar} 2.0.3: End-to-End Automated Machine Learning and Model Evaluation - diffify

• {edfinr} 0.2.0: Access Tidy Education Finance Data - diffify

• {chiOpenData} 0.1.1: Convenient Access to Chicago Open Data API Endpoints - diffify

• {biglasso} 1.7.0: Extending Lasso Model Fitting to Big Data - diffify

• {agregR} 1.0.4: Bayesian State-Space Aggregation of Brazilian Presidential Polls - diffify

• {actiread} 0.5.0: Baseline Package for Reading Actigraphy and Activity Data - diffify

• {RivRetrieve} 0.2.0: Retrieve Global River Gauge Data - diffify

• {vayr} 1.1.0: Extensions for ‘ggplot2’ to Visualize as You Randomize - diffify

• {rtiktoken} 0.11.0.2: A Byte-Pair-Encoding (BPE) Tokenizer for OpenAI’s Large Language Models - diffify

• {REDCapExporter} 0.3.6: Automated Construction of R Data Packages from REDCap Projects - diffify

• {rbm25} 2.3.2: A Light Wrapper Around the ‘BM25’ ‘Rust’ Crate for Okapi BM25 Text Search - diffify

• {dbarts} 0.9-34: Discrete Bayesian Additive Regression Trees Sampler - diffify

• {awdb} 0.1.4: Query the USDA NWCC Air and Water Database REST API - diffify

• {regressinator} 0.3.1: Simulate and Diagnose (Generalized) Linear Models - diffify

• {stepcount} 0.6.0: Estimate Step Counts from ‘Accelerometry’ Data - diffify

• {schwabr} 0.1.5: ‘Schwab API’ Interface - diffify

• {RiskPortfolios} 2.1.8: Computation of Risk-Based Portfolios - diffify

• {dataganger} 0.8.0: Synthetic Data Doubles for Safer Prototyping - diffify

• {socviz} 2.0.0: Utilities and Data Sets for Data Visualization - diffify

• {shiny.fluent} 0.4.1: Microsoft Fluent UI for Shiny Apps - diffify

• {scimesh} 0.3.4: Headless Publication-Quality 3D Mesh Rendering Engine - diffify

• {ifo} 0.2.5: Client for the Ifo Institute Time Series - diffify

• {healthyR.ai} 0.1.2: The Machine Learning and AI Modeling Companion to ‘healthyR’ - diffify

• {gridmicrotex} 0.1.0: Native ‘LaTeX’ Math Rendering for Grid Graphics - diffify

• {qs2} 0.3.1: Efficient Serialization of R Objects - diffify

• {GTFSwizard} 1.2.1: Creating, Exploring, and Manipulating GTFS Files - diffify

• {cards} 0.9.0: Analysis Results Data - diffify

• {bridgr} 1.0.0: Bridging Data Frequencies for Timely Economic Forecasts - diffify

• {optree} 0.1.2: Hierarchical Runtime Configuration Management - diffify

• {NonCompart} 0.8.3: Noncompartmental Analysis for Pharmacokinetic Data - diffify

• {mvord} 1.2.7: Multivariate Ordinal Regression Models - diffify

• {hydrogeofetch} 2.0.1: Hydrologic Geospatial Fabric Extraction Tool Chain - diffify

• {httptest} 4.2.4: A Test Environment for HTTP Requests - diffify

• {healthiar} 0.2.5: Quantifying and Monetizing Health Impacts Attributable to Exposure - diffify

• {rpart.plot} 3.1.5: Plot ‘rpart’ Models: An Enhanced Version of ‘plot.rpart’ - diffify

• {plotmo} 3.7.1: Plot a Model’s Residuals, Response, and Partial Dependence Plots - diffify

• {Modeler} 3.4.10: Classes and Methods for Training and Using Binary Prediction Models - diffify

• {modeldata} 1.6.0: Data Sets Useful for Modeling Examples - diffify

• {klassR} 1.0.7: Classifications for Statistics Norway - diffify

• {glmertree} 0.2-7: Generalized Linear Mixed Model Trees - diffify

• {tractor.base} 3.5.2.1: Read, Manipulate and Visualise Magnetic Resonance Images - diffify

• {RNiftyReg} 2.8.6: Image Registration Using the ‘NiftyReg’ Library - diffify

• {arcpbf} 0.3.0: Process ArcGIS Protocol Buffer FeatureCollections - diffify

• {tipse} 2.1: Tipping Point Analysis for Survival Endpoints - diffify

• {targeted} 0.9.0: Targeted Inference - diffify

• {mlr3benchmark} 0.1.8: Analysis and Visualisation of Benchmark Experiments - diffify

• {worldbank} 0.10.0: Client for the ‘World Bank’ APIs - diffify

• {vitae} 0.7.0: Curriculum Vitae for R Markdown - diffify

• {tinysnapshot} 0.3.0: Snapshots for Unit Tests using the ‘tinytest’ Framework - diffify

• {tinycodet} 0.7.1: Functions to Help in your Coding Etiquette - diffify

• {summata} 0.12.0: Publication-Ready Summary Tables and Forest Plots - diffify

• {inDAGO} 1.0.4: A GUI for Dual and Bulk RNA-Sequencing Analysis - diffify

• {fable.bayesRecon} 0.2.0: Bayesian Reconciliation in the ‘fable’ Framework - diffify

• {spOccupancy} 0.8.1: Single-Species, Multi-Species, and Integrated Spatial Occupancy Models - diffify

• {RTMBdist} 1.0.6: Distributions Compatible with Automatic Differentiation by ‘RTMB’ - diffify

• {rPDBapi} 3.0.2: A Comprehensive Interface for Accessing the Protein Data Bank - diffify

• {ReDaMoR} 1.0.1: Relational Data Modeler - diffify

• {RColetum} 1.1.0: Access your Coletum’s Data from API - diffify

• {ramps} 0.6.19: Bayesian Geostatistical Modeling with RAMPS - diffify

• {psrwe} 3.2-2: PS-Integrated Methods for Incorporating Real-World Evidence in Clinical Studies - diffify

• {parafac4microbiome} 1.3.3: Parallel Factor Analysis Modelling of Longitudinal Microbiome Data - diffify

• {orgutils} 0.5-4: Helper Functions for Org Files - diffify

• {gtregression} 1.1.0: Tools for Creating Publication-Ready Regression Tables - diffify

• {altmeta} 4.4: Alternative Meta-Analysis Methods - diffify

• {mlr3inferr} 0.2.2: Inference on the Generalization Error - diffify

• {locaR} 0.3.0: A Set of Tools for Sound Localization - diffify

• {IssueTrackeR} 1.5.0: List Things to Do - diffify

• {IsoplotR} 7.0: Statistical Toolbox for Radiometric Geochronology - diffify

• {isocountry} 0.7.0: ISO 3166-1 Country Codes - diffify

• {huito} 0.2.7: Reproducible and Flexible Label Design - diffify

• {healthyAddress} 0.5.2: Convert Addresses to Standard Inputs - diffify

• {healthatlas} 0.2.3: Explore and Import ‘Metopio’ Health Atlas Data and Spatial Layers - diffify

Videos and Podcasts

• Listen to the R-Weekly Highlights Podcast

• R/Pharma Hangout - Analysis Results Data with {gtsummary} and {cards}

Shiny Apps

• BIOSZEN: Reproducible biological data analysis and visualization in R - Open-source R package and Shiny application for reproducible analysis, visualization, statistical testing, and microbial growth-curve characterization.

• {rewind} 0.2.0: Undo and Redo for ‘Shiny’ Applications

R Project Updates

Updates from R Core:

Call for Participation

• Announcing AI in Production 2027

Upcoming Events in 3 Months

Events in 3 Months:

• From Raw Data to Regulatory Results: Clinical Trial Programming in R using the pharmaverse workshop

• A list of R conferences and meetings

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

• Weekly R Workshops for Ukraine

Connect

Join the Data Science Learning Community

Quotes of the Week

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