Agile Data Science with R by Edwin Thoen
#RStats
bigbookofr.com/chapters/workflow.html

Top hole, this is. #rstats

CRAN updates: mixedBayes readaec robscale #rstats

CRAN: Package tvrmst Utilities for restricted mean survival time (RMST) and time-varying restricted mean survival time quantities computed from survival curves provided on a time grid. The package is model-agnostic and accepts only a time vector and survival matrices, returning RMST-based quantities and bootstrap summaries. For restricted mean survival time methodology, see Royston and Parmar (2013) &lt;<a href="https://doi.org/10.1186%2F1471-2288-13-152" target="_top">doi:10.1186/1471-2288-13-152</a>&gt;.

New CRAN package tvrmst with initial version 0.0.6
#rstats
https://cran.r-project.org/package=tvrmst

CRAN: Package snakeplot Visualize long timelines, extended sequences and temporally chained survey responses and experience sampling data using intuitive serpentine (snake) plots. Supports distribution bars, tick-mark plots, inter-item correlation arcs, faceted multi-construct panels, and daily time-of-day positioning for ecological momentary assessment data.

New CRAN package snakeplot with initial version 0.3.0
#rstats
https://cran.r-project.org/package=snakeplot

CRAN: Package scimetr Tools for quantitative research in scientometrics and bibliometrics. This package provides routines for importing bibliographic data from Clarivate Web of Science (&lt;<a href="https://www.webofscience.com/wos/" target="_top">https://www.webofscience.com/wos/</a>&gt;) and performing bibliometric analysis.

New CRAN package scimetr with initial version 1.2.0
#rstats
https://cran.r-project.org/package=scimetr

CRAN: Package ROCModels The receiver operating characteristic (ROC) curve is one of the most widely used tools for evaluating diagnostic and prognostic biomarkers across diverse scientific fields, particularly in medicine. Despite its ubiquity, ROC estimation and testing methods differ substantially in their assumptions and resulting curve properties. This package provides a unified framework for constructing, visualizing, and comparing parametric, nonparametric, semiparametric, and Bayesian ROC curves. 'ROCModels' helps researchers identify and implement ROC inference methods most suitable for their data. See the accompanying vignette 'ROCModels_Package_Doc' for a detailed introduction. Alonzo, T. A., and Pepe, M. S. (2002) &lt;<a href="https://doi.org/10.1093%2Fbiostatistics%2F3.3.421" target="_top">doi:10.1093/biostatistics/3.3.421</a>&gt;, Andrews, D. F., and Herzberg, A. M. (1985) &lt;<a href="https://doi.org/10.1007%2F978-1-4612-5098-2" target="_top">doi:10.1007/978-1-4612-5098-2</a>&gt;, Bamber, D. (1975) &lt;<a href="https://doi.org/10.1016%2F0022-2496%2875%2990001-2" target="_top">doi:10.1016/0022-2496(75)90001-2</a>&gt;, Cox, D. R. (1972) &lt;<a href="https://doi.org/10.1111%2Fj.2517-6161.1972.tb00899.x" target="_top">doi:10.1111/j.2517-6161.1972.tb00899.x</a>&gt;, Cox, D. R. (1975) &lt;<a href="https://doi.org/10.1093%2Fbiomet%2F62.2.269" target="_top">doi:10.1093/biomet/62.2.269</a>&gt;, DeLong, E. R., DeLong, D. M., and Clarke-Pearson, D. L. (1988) &lt;<a href="https://doi.org/10.2307%2F2531595" target="_top">doi:10.2307/2531595</a>&gt;, Dorfman, D. D., and Alf, E. (1969) &lt;<a href="https://doi.org/10.1016%2F0022-2496%2869%2990019-4" target="_top">doi:10.1016/0022-2496(69)90019-4</a>&gt;, Dorfman, D. D., Berbaum, K. S., and Metz, C. E. (1997) &lt;<a href="https://doi.org/10.1016%2Fs1076-6332%2897%2980013-x" target="_top">doi:10.1016/s1076-6332(97)80013-x</a>&gt;, Erkanli, A., Sung, L., and Stamey, J. D. (2006) &lt;<a href="https://doi.org/10.1002%2Fsim.2496" target="_top">doi:10.1002/sim.2496</a>&gt;, Faraggi, D., and Reiser, B. (2002) &lt;<a href="https://doi.org/10.1002%2Fsim.1228" target="_top">doi:10.1002/sim.1228</a>&gt;, Ghebremichael, M., and Habtemicael, S. (2018) &lt;<a href="https://doi.org/10.1080%2F02664763.2017.1420758" target="_top">doi:10.1080/02664763.2017.1420758</a>&gt;, Ghebremichael, M., and Michael, H. (2024) &lt;<a href="https://doi.org/10.1080%2F03610918.2022.2032159" target="_top">doi:10.1080/03610918.2022.2032159</a>&gt;, Ghebremichael, M., Michael, H., Tubbs, J., and Paintsil, E. (2019) &lt;<a href="https://doi.org/10.3844%2Fjmssp.2019.55.64" target="_top">doi:10.3844/jmssp.2019.55.64</a>&gt;, Gönen, M., and Heller, G. (2010) &lt;<a href="https://doi.org/10.1177%2F0272989X09360067" target="_top">doi:10.1177/0272989X09360067</a>&gt;, Gopalakrishnan, V., Bose, E., Nair, U., Cheng, Y., and Ghebremichael, M. (2020) &lt;<a href="https://doi.org/10.1186%2Fs12879-020-05458-w" target="_top">doi:10.1186/s12879-020-05458-w</a>&gt;, Green, D. M., and Swets, J. A. (1966, ISBN:0471324205), Gu, J., and Ghosal, S. (2009) &lt;<a href="https://doi.org/10.1016%2Fj.jspi.2008.09.014" target="_top">doi:10.1016/j.jspi.2008.09.014</a>&gt;, Gu, Y., Ghosal, S., and Roy, A. (2008) &lt;<a href="https://doi.org/10.1002%2Fsim.3366" target="_top">doi:10.1002/sim.3366</a>&gt;, Guidoum, A. C. (2020) &lt;<a href="https://doi.org/10.32614%2FCRAN.package.kedd" target="_top">doi:10.32614/CRAN.package.kedd</a>&gt;, &lt;<a href="https://doi.org/10.48550%2FarXiv.2012.06102" target="_top">doi:10.48550/arXiv.2012.06102</a>&gt;, Guo, B. (2015) &lt;<a href="https://d-scholarship.pitt.edu/23590/1/Guo_Ben_thesis_12-2014.pdf" target="_top">https://d-scholarship.pitt.edu/23590/1/Guo_Ben_thesis_12-2014.pdf</a>&gt;, Hanley, J. A., and McNeil, B. J. (1982) &lt;<a href="https://doi.org/10.1148%2Fradiology.143.1.7063747" target="_top">doi:10.1148/radiology.143.1.7063747</a>&gt;, Hsieh, F., and Turnbull, B. W. (1996) &lt;<a href="https://doi.org/10.1214%2Faos%2F1033066197" target="_top">doi:10.1214/aos/1033066197</a>&gt;, Hussain, E. (2012) &lt;<a href="https://doi.org/10.6000%2F1927-5129.2012.08.02.09" target="_top">doi:10.6000/1927-5129.2012.08.02.09</a>&gt;, Ishwaran, H., and James, L. F. (2002) &lt;<a href="https://doi.org/10.1198%2F106186002411" target="_top">doi:10.1198/106186002411</a>&gt;, Jokiel-Rokita, A., and Topolnicki, R. (2020) &lt;<a href="https://doi.org/10.1016%2Fj.csda.2019.106820" target="_top">doi:10.1016/j.csda.2019.106820</a>&gt;, Krzanowski, W. J., and Hand, D. J. (2009) &lt;<a href="https://doi.org/10.1201%2F9781439800225" target="_top">doi:10.1201/9781439800225</a>&gt;, Kundu, D., and Gupta, R. D. (2006) &lt;<a href="https://doi.org/10.1109%2FTR.2006.874918" target="_top">doi:10.1109/TR.2006.874918</a>&gt;, Lloyd, C. J. (1998) &lt;<a href="https://doi.org/10.1080%2F01621459.1998.10473797" target="_top">doi:10.1080/01621459.1998.10473797</a>&gt;, Lehmann, E. L. (1953) &lt;<a href="https://doi.org/10.1214%2Faoms%2F1177729080" target="_top">doi:10.1214/aoms/1177729080</a>&gt;, Metz, C. E., Herman, B. A., and Shen, J. H. (1998) &lt;<a href="https://doi.org/10.1002/(SICI)1097-0258(19980515)17:9%3C1033::AID-SIM784%3E3.0.CO;2-Z" target="_top">doi:10.1002/(SICI)1097-0258(19980515)17:9%3C1033::AID-SIM784%3E3.0.CO;2-Z</a>&gt;, Pepe, M. S. (2003) &lt;<a href="https://doi.org/10.1093%2Foso%2F9780198509844.001.0001" target="_top">doi:10.1093/oso/9780198509844.001.0001</a>&gt;, Pundir, S., and Amala, R. (2014) &lt;<a href="https://doi.org/10.22237%2Fjmasm%2F1398917940" target="_top">doi:10.22237/jmasm/1398917940</a>&gt;, Silverman, B. W. (2018) &lt;<a href="https://doi.org/10.1201%2F9781315140919" target="_top">doi:10.1201/9781315140919</a>&gt;, Yeo, I. K., and Johnson, R. A. (2000) &lt;<a href="https://doi.org/10.1093%2Fbiomet%2F87.4.954" target="_top">doi:10.1093/biomet/87.4.954</a>&gt;, Zhou, X. H., McClish, D. K., and Obuchowski, N. A. (2009) &lt;<a href="https://doi.org/10.1002%2F9780470906514" target="_top">doi:10.1002/9780470906514</a>&gt;, Zou, K. H., Hall, W. J., and Shapiro, D. E. (1997) &lt;<a href="https://doi.org/10.1002/(SICI)1097-0258(19971015)16:19%3C2143::AID-SIM655%3E3.0.CO;2-3" target="_top">doi:10.1002/(SICI)1097-0258(19971015)16:19%3C2143::AID-SIM655%3E3.0.CO;2-3</a>&gt;.

New CRAN package ROCModels with initial version 1.0.0
#rstats
https://cran.r-project.org/package=ROCModels

CRAN: Package r4sub The 'r4sub' package is a meta-package that installs and loads core packages of the R4SUB (R for Regulatory Submission) clinical submission readiness ecosystem. Loading 'r4sub' attaches 'r4subcore', 'r4subtrace', 'r4subscore', 'r4subrisk', 'r4subdata', and 'r4subprofile'.

New CRAN package r4sub with initial version 0.1.0
#rstats
https://cran.r-project.org/package=r4sub

CRAN: Package lumbermark Implements a fast and resistant divisive clustering algorithm which identifies a specified number of clusters: 'lumbermark' iteratively chops off sizeable limbs that are joined by protruding segments of a dataset's mutual reachability minimum spanning tree; see Gagolewski (2026) &lt;<a href="https://lumbermark.gagolewski.com/" target="_top">https://lumbermark.gagolewski.com/</a>&gt;. The use of a mutual reachability distance pulls peripheral points farther away from each other. When combined with the 'deadwood' package, it can act as an outlier detector. The 'Python' version of 'lumbermark' is available via 'PyPI'.

New CRAN package lumbermark with initial version 0.9.0
#rstats
https://cran.r-project.org/package=lumbermark

CRAN: Package golden Fast, flexible, patient-level microsimulation. Time-stepped simulation with a 'C++' back-end from user-supplied initial population, trajectories, hazards, and corresponding event transitions. User-defined aggregate time series histories are returned together with the final population. Designed for simulation of chronic diseases with continuous and evolving risk factors, but could easily be applied more generally.

New CRAN package golden with initial version 0.0.1
#rstats
https://cran.r-project.org/package=golden

CRAN: Package ebx Client library for the 'Earth Blox' API (&lt;<a href="https://api.earthblox.io/" target="_top">https://api.earthblox.io/</a>&gt;). Provides authentication and endpoints for interacting with 'Earth Blox' geospatial analytics services. Compatible with 'Shiny' applications.

New CRAN package ebx with initial version 1.0.0
#rstats
https://cran.r-project.org/package=ebx

CRAN: Package easyEWAS Tools for conducting epigenome-wide association studies (EWAS) and visualizing results. Users provide sample metadata and methylation matrices to run EWAS with linear models, linear mixed-effects models, or Cox models. The package supports downstream visualization, bootstrap validation, enrichment analysis, batch effect correction, and differentially methylated region (DMR) analysis with optional parallel computing. Methods are described in Wang et al. (2025) &lt;<a href="https://doi.org/10.1093%2Fbioadv%2Fvbaf026" target="_top">doi:10.1093/bioadv/vbaf026</a>&gt;, Johnson et al. (2007) &lt;<a href="https://doi.org/10.1093%2Fbiostatistics%2Fkxj037" target="_top">doi:10.1093/biostatistics/kxj037</a>&gt;, and Peters et al. (2015) &lt;<a href="https://doi.org/10.1186%2F1756-8935-8-6" target="_top">doi:10.1186/1756-8935-8-6</a>&gt;.

New CRAN package easyEWAS with initial version 1.0.1
#rstats
https://cran.r-project.org/package=easyEWAS

CRAN: Package dyadicMarkov Provides methods for analyzing dyadic interaction sequences using transition matrices within the Actor-Partner Interdependence Model. The package supports the computation of empirical transition counts, maximum likelihood estimation of transition probabilities and identification of interaction patterns in univariate and bivariate dyadic interaction sequences.

New CRAN package dyadicMarkov with initial version 0.1.0
#rstats
https://cran.r-project.org/package=dyadicMarkov

CRAN: Package a5R Bindings for the "A5 geospatial index" &lt;<a href="https://a5geo.org/" target="_top">https://a5geo.org/</a>&gt;. 'A5' partitions the Earth's surface into pentagonal cells across 31 resolution levels using an equal-area projection onto a dodecahedron. Provides functions for indexing coordinates to cells, traversing the cell hierarchy, computing cell boundaries, and compacting/uncompacting cell sets. Powered by the 'A5' 'Rust' crate via 'extendr'.

New CRAN package a5R with initial version 0.2.0
#rstats
https://cran.r-project.org/package=a5R

CRAN: Package WaveST An integrated wavelet-based spatial time series modelling framework designed to enhance predictive accuracy under noisy and nonstationary conditions by jointly exploiting multi-resolution (wavelet) information and spatial dependence. The package implements WaveSARIMA() (Wavelet Based Spatial AutoRegressive Integrated Moving Average model using regression features with forecast::auto.arima()) and WaveSNN() (Wavelet Based Spatial Neural Network model using neuralnet with hyperparameter search). Both functions support spatial transformation via a user-supplied spatial matrix, lag feature construction, MODWT-based wavelet sub-series feature generation, time-ordered train/test splitting, and performance evaluation (Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared (R²), and Mean Absolute Percentage Error (MAPE)), returning fitted models and actual vs predicted values for train and test sets. The package has been developed using the algorithm of Paul et al. (2023) &lt;<a href="https://doi.org/10.1007%2Fs43538-025-00581-1" target="_top">doi:10.1007/s43538-025-00581-1</a>&gt;.

New CRAN package WaveST with initial version 0.1.0
#rstats
https://cran.r-project.org/package=WaveST

New CRAN package RenyiExtropy with initial version 0.4.0
#rstats
https://cran.r-project.org/package=RenyiExtropy

New CRAN package causalplot with initial version 0.2.1
#rstats
https://cran.r-project.org/package=causalplot

Simon–Ehrlich wager what years would it have been won and lost https://en.wikipedia.org/wiki/Simon%E2%80%93Ehrlich_wager data from https://www.usgs.gov/centers/national-minerals-information-center/his... Simon–Ehrlich wager what years would it have been won and lost https://en.wikipedia.org/wiki/Simon%E2%80%93Ehrlich_wager data from https://www.usgs.gov/centers/national-minerals-information-center/...

#rstats #ggplot2 code at
gist.github.com/cavedave/614...

Ageirein: Clinical Data Analytics Platform | Atorus Research Ageirein, by Atorus, is an integrated clinical data analytics platform for biotechs and pharma. Unify data, accelerate insights, ensure GxP compliance.

Ageirein unifies programming, visualization, and compliance in one secure environment. Multilingual capabilities (SAS®, R, Python), real-time collaboration, GxP-compliant. Built for pharma and biotech teams. www.atorusresearch.com/ageirein/

#Ageirein #ClinicalAnalytics #GxPCompliance #RStats

Deploying boosted tree models with Orbital - Posit Orbital 0.5.0 translates tidymodels workflows into SQL, featuring new tuning tools to balance predictive accuracy with database performance.

Our latest blog post shows we can deploy boosted tree models, such as xgboost, lightgbm, or catboost, directly into the database itself!

Running predictionsfrom an xgboost model on 2.1 million observations in less than 10 seconds!

posit.co/blog/deployi...
#rstats #tidymodels #mlops

No-code tools might become obsolete.

Mike Stackhouse shares a provocative take: Why build complex no-code architectures when AI can help anyone prototype directly with code?

Full episode: buff.ly/QUchTJa

Tune in and share your thoughts in the comments.

#Rstats #AI

CRAN readmissions: ergmclust #rstats

CRAN updates: LaMa mlflow #rstats

CRAN: Package widr Interface to the World Inequality Database (WID) API &lt;<a href="https://wid.world" target="_top">https://wid.world</a>&gt;. Downloads distributional national accounts data with filters for country, year, percentile, age group, and population type. Includes code validation and reference tables. Independent implementation unaffiliated with the World Inequality Lab (WIL) or the Paris School of Economics.

New CRAN package widr with initial version 0.1.0
#rstats
https://cran.r-project.org/package=widr

CRAN: Package UnitMix Tools to detect and correct measurement-unit errors in multivariate numeric data using model-based clustering. Gaussian mixture models with user-defined translation vectors identify clusters of records that differ in scale or unit. Core functionality includes cluster assignment via the EM algorithm, error correction based on posterior probabilities and pairwise scatterplot visualizations. For more details see Di Zio, Guarnera and Luzi (2005) &lt;<a href="https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2005001/article/8087-eng.pdf" target="_top">https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2005001/article/8087-eng.pdf</a>&gt;.

New CRAN package UnitMix with initial version 0.0.1
#rstats
https://cran.r-project.org/package=UnitMix

CRAN: Package rmedsem Conducts mediation analysis for structural equation models (SEM) estimated with 'lavaan', 'blavaan', 'cSEM', or 'modsem'. Implements the Baron and Kenny (1986) &lt;<a href="https://doi.org/10.1037%2F0022-3514.51.6.1173" target="_top">doi:10.1037/0022-3514.51.6.1173</a>&gt; and Zhao, Lynch &amp; Chen (2010) &lt;<a href="https://doi.org/10.1086%2F651257" target="_top">doi:10.1086/651257</a>&gt; approaches to determine the presence and type of mediation. Supports covariance-based SEM, partial least squares SEM, Bayesian SEM, and moderated mediation models. Reports indirect effects with standard errors from Sobel, Delta, Monte-Carlo, and bootstrap methods, along with effect size measures (RIT, RID).

New CRAN package rmedsem with initial version 1.0.0
#rstats
https://cran.r-project.org/package=rmedsem

CRAN: Package rangen Provides a collection of random number generators for common and custom distributions, along with utility functions for sampling and simulation.

New CRAN package rangen with initial version 0.0.1
#rstats
https://cran.r-project.org/package=rangen

CRAN: Package MultiSpline Provides tools for fitting, predicting, and visualizing nonlinear relationships in single-level, multilevel, and longitudinal regression models. Nonlinear functional forms are represented using natural cubic splines from 'splines' and smooth terms from 'mgcv'. The package offers a unified interface for specifying nonlinear effects, interactions with time variables, random-intercept clustering structures, and additional linear covariates. Utilities are included to generate prediction grids and produce effect plots, facilitating interpretation and visualization of nonlinear relationships in applied regression workflows. The implementation builds on established methods for spline-based regression and mixed-effects modeling (Hastie and Tibshirani, 1990 &lt;<a href="https://doi.org/10.1201%2F9780203738535" target="_top">doi:10.1201/9780203738535</a>&gt;; Bates et al., 2015 &lt;<a href="https://doi.org/10.18637%2Fjss.v067.i01" target="_top">doi:10.18637/jss.v067.i01</a>&gt;; Wood, 2017 &lt;<a href="https://doi.org/10.1201%2F9781315370279" target="_top">doi:10.1201/9781315370279</a>&gt;). Applications include hierarchical and longitudinal data structures common in education, health, and social science research.

New CRAN package MultiSpline with initial version 0.1.1
#rstats
https://cran.r-project.org/package=MultiSpline

CRAN: Package mtgjsonsdk Auto-downloads Parquet data from the 'MTGJSON' CDN and exposes the full Magic: The Gathering dataset through R6-based query interfaces backed by 'DuckDB'.

New CRAN package mtgjsonsdk with initial version 0.1.0
#rstats
https://cran.r-project.org/package=mtgjsonsdk

CRAN: Package MSMGOptimizer A 'Shiny' application for converting 'Excel'-based Life Cycle Inventory (LCI) data into 'SimaPro' CSV (Comma-Separated Values) format for use in Life Cycle Assessment (LCA) modeling. Developed by the Mine Sustainability Modeling Group (MSMG) at Missouri University of Science and Technology under NSF (National Science Foundation) funding (Award No. 2219086). See Pizzol (2022) &lt;<a href="https://github.com/massimopizzol/Simapro-CSV-converter" target="_top">https://github.com/massimopizzol/Simapro-CSV-converter</a>&gt; for the original 'Python' implementation that inspired this tool.

New CRAN package MSMGOptimizer with initial version 0.1.0
#rstats
https://cran.r-project.org/package=MSMGOptimizer