#Tag · bonfire.cafe

I’m thrilled to introduce flownet (https://sebkrantz.github.io/flownet/), a new R package for transport modeling, supporting stochastic or deterministic traffic assignment to large networks, and powerful tools for (multimodal) network processing/simplification. Read the release blog post at https://sebkrantz.github.io/Rblog/2026/02/09/introducing-flownet-efficient-transport-modeling-in-r/ #Rstats #DataScience

Introducing flownet: Efficient Transport Modeling in R - R, Econometrics, High Performance

Transport Modeling in R

High-performance tools for transport modeling - network processing, route enumeration, and traffic assignment in R. The package implements the Path-Sized Logit model for traffic assignment - Ben-Akiva and Bierlaire (1999) - an efficient route enumeration algorithm, and provides powerful utility functions for (multimodal) network generation, consolidation/contraction, and/or simplification. The user is expected to provide a transport network (either a graph or collection of linestrings) and an origin-destination (OD) matrix of trade/traffic flows. Maintained by transport consultants at CPCS (cpcs.ca).

Sebastian Krantz

@sebkrantz@fosstodon.org · 10 hours ago

Introducing flownet: Efficient Transport Modeling in R - R, Econometrics, High Performance

Transport Modeling in R

Felix Schönbrodt boosted

Mickaël CANOUIL

@MickaelCanouil@fosstodon.org · 2 months ago

Offcanvas 1.0.0 for Quarto is now available.

Simple syntax for Bootstrap offcanvas components using fenced divs.
Automatically converts margin content to collapsible panels on mobile, keeping aside notes accessible where they're referenced.
https://github.com/mcanouil/quarto-offcanvas
#Quarto #DataScience #Bootstrap

GitHub

GitHub - mcanouil/quarto-offcanvas: Quarto extension to create Bootstrap offcanvas components in HTML documents.

Quarto extension to create Bootstrap offcanvas components in HTML documents. - mcanouil/quarto-offcanvas

Chip Butty boosted

Dirk Eddelbuettel

@eddelbuettel@mastodon.social · yesterday

chronometre: A new package (pair) demo for R and Python
Efficient yet simple object exchange between R and Python
https://dirk.eddelbuettel.com/blog/2026/02/08#chronometre_initial_pair
#rstats #rcpp #python #datascience

3 media

Screenshot one of three describe a new pair of R and Python packages demonstrating easy object interchange between R and Python.

Screenshot two of three describe a new pair of R and Python packages demonstrating easy object interchange between R and Python.

Screenshot three of three describe a new pair of R and Python packages demonstrating easy object interchange between R and Python.

Thinking inside the box

Dirk Eddelbuettel, R, C++, Rcpp

Dirk Eddelbuettel

@eddelbuettel@mastodon.social · yesterday

3 media

Thinking inside the box

Dirk Eddelbuettel, R, C++, Rcpp

Yani Bellini Saibene boosted

R Consortium

@RConsortium@fosstodon.org · 2 days ago

Risk 2026 is coming up in two weeks! Full schedule available!

R is used to calculate, measure, report, or mitigate risk. Find out the latest techniques, ask your questions directly to speakers, connect with other R users. Reproducibility, AI, Security, risk visuals...

https://rconsortium.github.io/Risk_website/

#rstats #risk #risk_management #dataScience

Risk 2026

R Consortium

@RConsortium@fosstodon.org · 2 days ago

Risk 2026 is coming up in two weeks! Full schedule available!

https://rconsortium.github.io/Risk_website/

#rstats #risk #risk_management #dataScience

Risk 2026

Federica Gazzelloni

@fgazzelloni@fosstodon.org · 3 days ago

📬 Issue No. 5 of Notes on Data and Learning highlights the skill of omission in education.

In teaching data, statistics, or modeling, deciding what to exclude is as crucial as what to include, preventing overwhelm while keeping depth.

Drawing from my Health Metrics and Infectious Diseases experience, I discuss how teaching and writing prioritize over completeness.

👉 Full issue on Substack: https://federicagazzelloni.substack.com

#Teaching #Learning #DataScience #Education #Substack

'(vidak) _(:3」∠)_ boosted

Rob Pumphrey

@robpumphrey@mastodonapp.uk · 3 days ago

This week's data science gag in Private Eye.

#Humour #Joke #DataScience

A cartoon barchart with the title "Do you feel intimidated?"
A large orange bar is labelled No
A tiny blue bar is labelled Yes — A cartoon barchart with the title "Do you feel intimidated?" A large orange bar is labelled No A tiny blue bar is labelled Yes

Rob Pumphrey

@robpumphrey@mastodonapp.uk · 3 days ago

This week's data science gag in Private Eye.

#Humour #Joke #DataScience

Gjalt-Jorn Peters and 1 other boosted

Fedi.Garden

@FediGarden@social.growyourown.services · 5 days ago

Datasci.social is a Mastodon server for researchers & practitioners in human-centric data science, broadly defined. For example: human-centric network science, social data science, computational social science, geospatial data science.

https://datasci.social

You can find out more at https://datasci.social/about or contact the admin account @mszll

#FeaturedServer #DataScience #DataSci #Mastodon #Fediverse #FreeFediverse

Gjalt-Jorn Peters boosted

R-Podcast (Eric) :pci:

@rpodcast@podcastindex.social · 6 days ago

Episode 220 of the @rstats @rweekly Highlights Podcast is out! https://serve.podhome.fm/episodepage/r-weekly-highlights/220

🎙️ @emilyriederer shares her data science insights on the Test Set @Posit
💪 Git for data workflows @fgazzelloni @rladiesrome
🏗️ libdplyr @mrchypark

Spreading the word of the R Weekly project, contributing your favorite resource to the site, or sending your hosts a fun boost are excellent ways to participate in #value4value!

h/t @mike_thomas & @jonocarroll 🙏

#RStats #DataScience

R Weekly Highlights

Issue 2026-W06 Highlights

A prominent leader at the intersection of data science and data engineering across multiple languages shares her insights, how a recent Git workshop…

Fedi.Garden

@FediGarden@social.growyourown.services · 5 days ago

https://datasci.social

You can find out more at https://datasci.social/about or contact the admin account @mszll

#FeaturedServer #DataScience #DataSci #Mastodon #Fediverse #FreeFediverse

R-Podcast (Eric) :pci:

@rpodcast@podcastindex.social · 6 days ago

h/t @mike_thomas & @jonocarroll 🙏

#RStats #DataScience

R Weekly Highlights

Issue 2026-W06 Highlights

A prominent leader at the intersection of data science and data engineering across multiple languages shares her insights, how a recent Git workshop…

devSJR :python: :rstats: and 1 other boosted

Bruno Rodrigues :rstats: :tux:

@brodriguesco@fosstodon.org · last week

The first data science book that has a chapter on monads https://reproducible-data-science.dev/

Learn how to build robust #DataScience pipelines with #RStats, #Python , #Julia and #Nix !

Fabrizio Musacchio

@FabMusacchio@mastodon.social · last week

Looking at #correlation plots, I sometimes see R² ("R-squared") reported instead of the Pearson correlation coefficient r. While R² = r² holds in simple linear regression, reporting R² alone can mislead by omitting directionality. I have summarized the key differences between r and R² and tried to give a best-practice guide on when to use each:

🌍 https://www.fabriziomusacchio.com/blog/2025-09-13-r_vs_r_squared/

#Python #Statistics #DataScience #Neuroscience

When to report r vs R^2: r quantifies the strength and direction of linear association between two variables, while R^2 measures the proportion of variance explained by a regression model.

Fabrizio Musacchio

Distinguishing correlation from the coefficient of determination: Proper reporting of r and R²

I noticed that people sometimes report R² (‘R-squared’) instead of the Pearson correlation coefficient r when discussing the correlation between two variables. In the special case of a simple linear relationship this numerical equality is not strictly wrong, yet presenting R² as if it were the correlation coefficient might wrongly give the impression they are the same thing. In this post, we will therefore unpack the difference between these two measures, explain their mathematical definitions and proper usage discuss the best practices for when to use each in statistical reporting.

Bruno Rodrigues :rstats: :tux:

@brodriguesco@fosstodon.org · last week

The first data science book that has a chapter on monads https://reproducible-data-science.dev/

Learn how to build robust #DataScience pipelines with #RStats, #Python , #Julia and #Nix !

Fabrizio Musacchio

@FabMusacchio@mastodon.social · last week

Due to a recent discussion with colleagues on whether and when to use #LinearMixedModels ( #LMM), I wrote a blog post comparing LMM to other approaches using simulated data. I thought, it may also be useful for others working with hierarchical data structures in #neuroscience and beyond.

🌍 https://www.fabriziomusacchio.com/blog/2026-01-31-linear_mixed_models/

#Python #Statistics #DataScience #MixedModels #Statsmodels #ANOVA #ANCOVA #GLMM #regression

2 media

Line plot of simulated neural responses versus stimulus strength for three subjects, with residuals and group comparisons.

Group specific slope comparison in an LMM friendly regime with many groups and few observations per group. ANCOVA interaction slope estimates scatter widely, while LMM BLUP slopes are pulled toward the population mean.

Fabrizio Musacchio

Linear mixed models in practice: When ANCOVA is enough and when you really need random effects

Linear mixed models (LMMs) are a powerful statistical tool for analyzing hierarchical or grouped data, common in neuroscience experiments. This post provides a practical guide on when to use LMMs versus traditional ANCOVA approaches, highlighting the advantages of mixed models in handling dependencies, unbalanced designs, and stabilizing estimates through shrinkage. Through simulated examples, we illustrate the differences in model performance and interpretation, helping you to make informed decisions about your statistical analyses.