👩‍💻📊 Sur un #R du concours #MinesPonts pour les #CPGE scientifiques à l'aide du R pkg plotly, petit outil interactif (1e vers°) que j'ai créé pr aider élèves & profs à avoir une information résumée du nbre de places par filière en 2025 (attente des stats2026).

Source: SCEI

#rstats #dataesr #dataviz

CRAN: Package roam Provide helper functions for package developers to create active bindings that looks like data embedded in the package, but are downloaded from remote sources.

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

Object not found! https://cran.r-project.org/package=LactCurveModels

New CRAN package LactCurveModels with initial version 0.1.5
#rstats
https://cran.r-project.org/package=LactCurveModels

CRAN: Package kuenm2 A new set of tools to help with the development of detailed ecological niche models using multiple algorithms. Pre-modeling analyses and explorations can be done to prepare data. Model calibration (model selection) can be done by creating and testing models with several parameter combinations. Handy options for producing final models with transfers are included. Other tools to assess extrapolation risks and variability in model transfers are also available. Methodological and theoretical basis for the methods implemented here can be found in: Peterson et al. (2011) &lt;<a href="https://www.degruyter.com/princetonup/view/title/506966" target="_top">https://www.degruyter.com/princetonup/view/title/506966</a>&gt;, Radosavljevic and Anderson (2014) &lt;<a href="https://doi.org/10.1111%2Fjbi.12227" target="_top">doi:10.1111/jbi.12227</a>&gt;, Peterson et al. (2018) &lt;<a href="https://doi.org/10.1111%2Fnyas.13873" target="_top">doi:10.1111/nyas.13873</a>&gt;, Cobos et al. (2019) &lt;<a href="https://doi.org/10.7717%2Fpeerj.6281" target="_top">doi:10.7717/peerj.6281</a>&gt;, Alkishe et al. (2020) &lt;<a href="https://doi.org/10.1016%2Fj.pecon.2020.03.002" target="_top">doi:10.1016/j.pecon.2020.03.002</a>&gt;, Machado-Stredel et al. (2021) &lt;<a href="https://doi.org/10.21425%2FF5FBG48814" target="_top">doi:10.21425/F5FBG48814</a>&gt;, Arias-Giraldo and Cobos (2024) &lt;<a href="https://doi.org/10.17161%2Fbi.v18i.21742" target="_top">doi:10.17161/bi.v18i.21742</a>&gt;, Cobos et al. (2024) &lt;<a href="https://doi.org/10.17161%2Fbi.v18i.21742" target="_top">doi:10.17161/bi.v18i.21742</a>&gt;.

New CRAN package kuenm2 with initial version 0.1.2
#rstats
https://cran.r-project.org/package=kuenm2

CRAN: Package ggExametrika Provides 'ggplot2'-based visualization functions for output objects from the 'exametrika' package, which implements test data engineering methods described in Shojima (2022, ISBN:978-981-16-9547-1). Supports a wide range of psychometric models including Item Response Theory, Latent Class Analysis, Latent Rank Analysis, Biclustering (binary, ordinal, and nominal), Bayesian Network Models, and related network models. All plot functions return 'ggplot2' objects that can be further customized by the user.

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

CRAN: Package FluxSeparator Separates diffusive and ebullitive (bubble) fluxes from continuous concentration measurements using a running variance approach. Ebullitive events are identified when the running variance exceeds a user-set threshold. Diffusive fluxes are calculated via linear regression on the non-ebullitive portion of the data. See Sø et al. (2024) &lt;<a href="https://doi.org/10.1029%2F2024JG008035" target="_top">doi:10.1029/2024JG008035</a>&gt; for details.

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

CRAN: Package evmr Tools for extreme value modeling based on the r-largest order statistics framework. The package provides functions for parameter estimation via maximum likelihood, return level estimation with standard errors, profile likelihood-based confidence intervals, random sample generation, and entropy difference tests for selecting the number of order statistics r. Several r-largest order statistics models are implemented, including the four-parameter kappa (rK4D), generalized logistic (rGLO), generalized Gumbel (rGGD), logistic (rLD), and Gumbel (rGD) distributions. The rK4D methodology is described in Shin et al. (2022) &lt;<a href="https://doi.org/10.1016%2Fj.wace.2022.100533" target="_top">doi:10.1016/j.wace.2022.100533</a>&gt;, the rGLO model in Shin and Park (2024) &lt;<a href="https://doi.org/10.1007%2Fs00477-023-02642-7" target="_top">doi:10.1007/s00477-023-02642-7</a>&gt;, and the rGGD model in Shin and Park (2025) &lt;<a href="https://doi.org/10.1038%2Fs41598-024-83273-y" target="_top">doi:10.1038/s41598-024-83273-y</a>&gt;. The underlying distributions are related to the kappa distribution of Hosking (1994) &lt;<a href="https://doi.org/10.1017%2FCBO9780511529443" target="_top">doi:10.1017/CBO9780511529443</a>&gt;, the generalized logistic distribution discussed by Ahmad et al. (1988) &lt;<a href="https://doi.org/10.1016%2F0022-1694%2888%2990015-7" target="_top">doi:10.1016/0022-1694(88)90015-7</a>&gt;, and the generalized Gumbel distribution of Jeong et al. (2014) &lt;<a href="https://doi.org/10.1007%2Fs00477-014-0865-8" target="_top">doi:10.1007/s00477-014-0865-8</a>&gt;. Penalized likelihood approaches for extreme value estimation follow Martins and Stedinger (2000) &lt;<a href="https://doi.org/10.1029%2F1999WR900330" target="_top">doi:10.1029/1999WR900330</a>&gt; and Coles and Dixon (1999) &lt;<a href="https://doi.org/10.1023%2FA%3A1009905222644" target="_top">doi:10.1023/A:1009905222644</a>&gt;. Selection of r is supported using methods discussed in Bader et al. (2017) &lt;<a href="https://doi.org/10.1007%2Fs11222-016-9697-3" target="_top">doi:10.1007/s11222-016-9697-3</a>&gt;. The package is intended for hydrological, climatological, and environmental extreme value analysis.

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

CRAN: Package ameras Analyze association studies with multiple realizations of a noisy or uncertain exposure. These can be obtained from e.g. a two-dimensional Monte Carlo dosimetry system (Simon et al 2015 &lt;<a href="https://doi.org/10.1667%2FRR13729.1" target="_top">doi:10.1667/RR13729.1</a>&gt;) to characterize exposure uncertainty. The implemented methods are regression calibration (Carroll et al. 2006 &lt;<a href="https://doi.org/10.1201%2F9781420010138" target="_top">doi:10.1201/9781420010138</a>&gt;), extended regression calibration (Little et al. 2023 &lt;<a href="https://doi.org/10.1038%2Fs41598-023-42283-y" target="_top">doi:10.1038/s41598-023-42283-y</a>&gt;), Monte Carlo maximum likelihood (Stayner et al. 2007 &lt;<a href="https://doi.org/10.1667%2FRR0677.1" target="_top">doi:10.1667/RR0677.1</a>&gt;), frequentist model averaging (Kwon et al. 2023 &lt;<a href="https://doi.org/10.1371%2Fjournal.pone.0290498" target="_top">doi:10.1371/journal.pone.0290498</a>&gt;), and Bayesian model averaging (Kwon et al. 2016 &lt;<a href="https://doi.org/10.1002%2Fsim.6635" target="_top">doi:10.1002/sim.6635</a>&gt;). Supported model families are Gaussian, binomial, multinomial, Poisson, proportional hazards, and conditional logistic.

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

Practical R for Mass Communication and Journalism by Sharon Machlis
#RStats
bigbookofr.com/chapters/journalism.html

CRAN readmissions: bioregion leaflet.extras TOU #rstats

CRAN updates: BORG DER evanverse opensimplex2 #rstats

CRAN: Package xtrec Implements the recursively detrended panel unit root tests proposed by Westerlund (2015) &lt;<a href="https://doi.org/10.1016%2Fj.jeconom.2014.09.013" target="_top">doi:10.1016/j.jeconom.2014.09.013</a>&gt;. Two variants are provided: the basic t-REC test assuming iid errors, and the robust t-RREC test that accounts for serial correlation, cross-sectional dependence, and heteroskedasticity via defactoring and BIC-selected lag augmentation. Both tests have a standard normal null distribution requiring no mean or variance correction. The panel must be strongly balanced.

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

Object not found! https://cran.r-project.org/package=simpowa

New CRAN package simpowa with initial version 1.0.3
#rstats
https://cran.r-project.org/package=simpowa

CRAN: Package flexCausal Provides doubly robust one-step and targeted maximum likelihood (TMLE) estimators for average causal effects in acyclic directed mixed graphs (ADMGs) with unmeasured variables. Automatically determines whether the treatment effect is identified via backdoor adjustment or the extended front-door functional, and dispatches to the appropriate estimator. Supports incorporation of machine learning algorithms via 'SuperLearner' and cross-fitting for nuisance estimation. Methods are described in Guo and Nabi (2024) &lt;<a href="https://doi.org/10.48550%2FarXiv.2409.03962" target="_top">doi:10.48550/arXiv.2409.03962</a>&gt;.

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

CRAN: Package fishmechr Processes tracked points on a fish's body and uses them to estimate standard kinematic parameters such as tail beat amplitude and frequency, body wavelength and wavespeed. As part of this, it also estimates the location of the center of mass and the principal swimming axis. The techniques are described in detail in the main vignette and are published in the book chapter Hawkins, O.H., Di Santo, V., Tytell, Eric.D., 2025. "Biomechanics and energetics of swimming", in: Higham, T.E., Lauder, G.V. (Eds.), Integrative Fish Biomechanics, Fish Physiology. Academic Press. &lt;<a href="https://doi.org/10.1016%2Fbs.fp.2025.06.003" target="_top">doi:10.1016/bs.fp.2025.06.003</a>&gt;.

New CRAN package fishmechr with initial version 1.0.3
#rstats
https://cran.r-project.org/package=fishmechr

CRAN: Package FinanceGraphs Flexible wrappers around R graphics modules 'dygraphs' &lt;<a href="https://dygraphs.com/" target="_top">https://dygraphs.com/</a>&gt; and 'ggplot2' &lt;<a href="https://ggplot2.tidyverse.org/" target="_top">https://ggplot2.tidyverse.org/</a>&gt; to visualize data commonly found in Financial Studies, with an emphasis on time series. Interactive time series plots include multiple options for incorporating external data such as forecasts and events. Other static plots useful for time series data include an intuitive and generic scatter plotter, a boxplot generator suitable for multiple time series, and event study plotters for time series analysis around sets of dates.

New CRAN package FinanceGraphs with initial version 0.8.0
#rstats
https://cran.r-project.org/package=FinanceGraphs

CRAN: Package drMAIC Implements Doubly Robust Matching-Adjusted Indirect Comparison (DR-MAIC) for population-adjusted indirect treatment comparisons in health technology appraisal (HTA). The package provides: (1) standard MAIC via entropy balancing / exponential tilting; (2) augmented/doubly robust MAIC combining inverse probability weighting with outcome regression; (3) comprehensive covariate balance diagnostics including standardised mean differences, Love plots, and effective sample size; (4) sensitivity analyses including E-values, weight trimming, and variable exclusion analyses; (5) bootstrap confidence intervals; and (6) submission-ready outputs aligned with NICE Decision Support Unit Technical Support Document 18, Cochrane Handbook guidance on indirect comparisons, and ISPOR best practice guidelines. Supports binary (risk difference, risk ratio, odds ratio) and time-to-event (hazard ratio) outcomes.

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

CRAN: Package CellDEEP Pool cells together before running differentially expression (DE) analysis. Tell 'CellDEEP' how many cells you want to pool together (which shall be determined by the overall cell number of data), then run DE analysis. Cheng et al. (2026) &lt;<a href="https://doi.org/10.64898%2F2026.03.09.710522" target="_top">doi:10.64898/2026.03.09.710522</a>&gt;.

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

CRAN: Package bayespmtools Performs Bayesian sample size, precision, and value-of-information analysis for external validation of existing multi-variable prediction models using the approach proposed by Sadatsafavi and colleagues (2025) &lt;<a href="https://doi.org/10.1002%2Fsim.70389" target="_top">doi:10.1002/sim.70389</a>&gt;.

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

9 International Trade of EU | Using Eurostat with R 9 International Trade of EU | Using Eurostat with R

@astavrakoudis.bsky.social has a nice introduction to Eurostat trade data
#rstats #econsky

What are you looking for? Comtrade data?

stavrakoudis.econ.uoi.gr/r-eurostat/i...

library(tidyplots) gene_expression |> tidyplot(x = group, y = expression, color = group) |> add_violin() |> add_data_points_beeswarm(white_border = TRUE) |> view_plot(data = filter_rows(external_gene_name == "Apol6"), title = "Apol6")

This is how you can preview a subset of the dataset in #tidyplots 🔎

#rstats #dataviz #phd

DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor AbstractSummary. Modern omics experiments now involve multiple conditions and complex designs, producing an increasingly large set of differential expressi

From @guigadraws.bsky.social & colleagues in #OUP 's #Bioinformatics journal | DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor | #RStats #Bioconductor #Bioinformatics #OpenScience | 🧬 🖥️ 🧪🔓
⬇️
academic.oup.com/bioinformati...

CRAN updates: GAReg HDANOVA #rstats

CRAN: Package rjd3bench Interface to 'JDemetra+' 3.x (&lt;<a href="https://github.com/jdemetra" target="_top">https://github.com/jdemetra</a>&gt;) time series analysis software. It provides a variety of methods for temporal disaggregation &amp; interpolation, benchmarking, reconciliation and calendarization. It incorporates statistical methods described in the latest European Statistical System (ESS) guidelines on temporal disaggregation, benchmarking, and reconciliation (2018 edition). The package implements highly efficient algorithms for fast and reliable computation.

New CRAN package rjd3bench with initial version 3.1.1
#rstats
https://cran.r-project.org/package=rjd3bench

Object not found! https://cran.r-project.org/package=reviser

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

CRAN: Package betaARMA Fits Beta Autoregressive Moving Average (BARMA) models for time series data distributed in the standard unit interval (0, 1). The estimation is performed via the conditional maximum likelihood method using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton algorithm. The package includes tools for model fitting, diagnostic checking, and forecasting. Based on the work of Rocha and Cribari-Neto (2009) &lt;<a href="https://doi.org/10.1007%2Fs11749-008-0112-z" target="_top">doi:10.1007/s11749-008-0112-z</a>&gt; and the associated erratum Rocha and Cribari-Neto (2017) &lt;<a href="https://doi.org/10.1007%2Fs11749-017-0528-4" target="_top">doi:10.1007/s11749-017-0528-4</a>&gt;. The original code was developed by Fabio M. Bayer.

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

Object not found! https://cran.r-project.org/package=autoMR

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

Check out the Statistics Globe Hub: statisticsglobe.com/hub

The Statistics Globe Hub is an ongoing learning program that helps you stay up to date with statistics, data science, AI, and programming using R and Python.

#tidyplots #datavisualization #rstats #ggplot2 #datascience #statisticsglobehub

CRAN updates: scplot #rstats

I’ve gone on the full journey from scripts, to this (whole pipeline in one RMD, no caching) to running into real problems (+30 minutes for small analysis changes), then to bespoke invalidated caching scripts*, and finally landing on {targets}.

*writing my own worse version of {targets}

#RStats