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Just for me visionscience + stats + rstats + cogsci and multiple block lists

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Isabella Velásquez
@ivelasq3.bsky.social
about 7 hours ago
I'm super excited to give a workshop during next week's R/Pharma conference! Join me if you'd like to discuss the ✨beautiful✨ outputs you can create with #Quarto and #RStats. 📅 Tuesday, November 4 at 9 am CT 🔗 Free to register, too! events.zoom.us/ev/Ai-geyS63...
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Ingo Rohlfing
@ingorohlfing.bsky.social
about 3 hours ago
{DAGassist} let's you process a DAG and estimate models giving {dagitty} input and baseline model #CausalSky #rstats cran.r-project.org/web/packages... I think {ggdag} has more to offer for analysis of a DAG, while {DAGassist} is a one-stop package for classifying variables and getting estimates
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Hockey Reference
@hockey-reference.com
about 3 hours ago
2025-2026 Points Leaders: 19 - Jack Eichel 17 - Nathan MacKinnon 17 - Nick Schmaltz 16 - Macklin Celebrini 16 - Dylan Larkin www.hockey-reference.com/…
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Jadey Ryan
@jadeynryan.bsky.social
about 4 hours ago
The recording is up if you weren't able to catch this session live and want a walk-through of how to use {fs} to clean up your messy folders! Thanks again @rladies-bot.bsky.social for the invitation to get back into the #RStats community after my maternity hiatus! 💜

Thank you so much to @jadeynryan.bsky.social for the fantastic workshop last night. The recording for Efficient File Management in R with {fs} is now available! youtu.be/X4i-yOBtn1s

youtu.be

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terence
@tterence.bsky.social
about 10 hours ago
Continuing with forest canopy heights, here's one of Switzerland. #rayshader adventures, an #rstats tale
A visualisation of Switzerland's forest canopy heights
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Clarity Data Studio
@claritydatastudio.com
about 7 hours ago
Over the past 1 year, we've switched to Typst for creating PDF reports, because it enhances flexibility and client usability. For JHU's IVAC reports, Typst allowed us to design state-specific exemption boxes. See how we did it + Typst tips by @joseph-barbier.bsky.social. buff.ly/PV7zVMZ #rstats

youtu.be

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Dariia Mykhailyshyna
@dariia.bsky.social
about 7 hours ago
❗️Our next workshop will be on Nov 6 6 pm CET titled Linear algebra using Armadillo via armadillo4r Register or sponsor a student by donating to support Ukraine! Details: bit.ly/3wBeY4S Please share! #AcademicSky #EconSky #RStats
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Ben Balas
@bjbalas.bsky.social
about 7 hours ago
Another addition to my #VisionScience teaching website: A new hands-on demo for DIY Binocular Rivalry! All you need are anaglyph glasses & matching colored pencils to watch your two eyes fight for your visual awareness. Students really like this one and it's super quick to do. #STEMEducation
Vision Demos: DIY Binocular Rivalry

docs.google.com

Vision Demos: DIY Binocular Rivalry

Vision Demos: DIY Binocular Rivalry Things you will need: A pair of 3D anaglyph glasses - I’ve found that Red/Green are easier to work with but Red/Cyan will also likely be good. Colored pencils or...

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Gavin Simpson
@gsimpson.bsky.social
1 day ago
Just updated my manuscript on using #GAMs in #AnimalScience, now on arXiv: doi.org/10.48550/arX... 🐄🐖🪶 Extended examples now show how GAMs go beyond prediction, helping estimate biologically meaningful traits from data. Code: github.com/gavinsimpson... 🧪 #RStats #mgcv #Statistics #OpenScience
Results of model fitting to the average daily fat content data from @Henderson1990-bd. a) observed average daily fat content (points) and estimated lactation curves from Wood's [-@Wood1967-re] model, a Tweedie GLM, and a Tweedie GAM (lines) with associated 95% confidence (Wood's model) or 95% credible intervals (GLM and GAM). Response residuals for Wood's model (b), Tweedie GLM (c), and Tweedie GAM (d), plus scatter plot smoothers (lines) and 95% credible intervals (shaded ribbons).

The fitted lactation curves are like an inverted U, with an extended longer tail to the right (later in lactation). The GAM curve fits the data well, but the fitted curves from Wood's model and the GLM equivalent do not provide good fits to the data, and over predict the amount of fat produced at the peak of lactation, and only grossly capture the decline in fat production later in lactation. The remaining panels show the raw response residuals for the three models, drawing attention to the poor fit; for Wood's model and the GLM there is significant pattern in the residuals, while for the GAM no residual pattern is observed.
Quantities of interest derived from Wood's model, a Tweedie GLM, and a Tweedie GAM fitted to the lactation data example: a) the estimated week of peak average daily fat content, b) the estimated average daily fat content at the peak, and c) the rate of change (first derivative) of the lactation curve estimated at a point that is midway between the peak fat content and the end of lacation. The points are the estimated values and the lines are a 95% uncertainty interval. The uncertainty interval is based on the 0.025 and 0.975 percentiles of the bootstrap distribution of model coefficient estimates (Wood's model) or of the posterior distribution (GLM and GAM).

Each panel shows three point estimates and an uncertainty range. The three points are the estimates from a GAM, a GLM, and Wood's lactation model. The first panel shows the estimated timing of the peak of lactation, with the GAM capturing the fact that the peak in the data occurs much later in lactation (~ week 11) while the other two models confidently estimate that the peak is in week ~8-9. The GAM estimate has a much wider credible interval, which does include the estimates of Wood's model & the GLM at the extreme end. This reflects the uncertainty in the estimation of the peak timing arising from the data having a wide flat peak.

The other panels show the estimates of fat content at the peak, which are broadly similar at ~ 0.7 kg fat per day. The final panel showing the persistency estimate shows the GAM estimate diverging from those of the GLM and Wood's model. Again, the latter two models are overly confident in their estimation of this biologically relevant parameter, despite the fited lactation curve not really following the lactation data.
a) Estimated daily growth rate on November 15^th^, 2021 and 95% Bayesian credible interval for the 18 pigs in the pig growth example. b) Posterior distribution of daily growth rate on November 15^th^, 2021, for three pigs (numbers 2, 13, and 17), for whom weight observations ceased before November 1^st^, 2021. In b), the shaded region is the posterior distribution, the point, and thick and thin bars are the posterior median, and 66% and 95% posterior intervals respectively.

With the fitted growth curves, we can estimate for any day what the growth rate of each pig was. In this figure I'm showing the estimated growth rate of each pig in the example on November 21st. This growth rate is the first derivative of the fitted growth curve (smooth function). I used posterior sampling to produce the posterior distribution of the growth rate for each pig. These are summarised as a point estimate (median) and ccredible interval in the first panel with most pigs growin at ~1-1.5 kg per day by November 21st, with uncertainties on the order of +/- 0.5 kg per day.

The second panel shows the entire posterior distribution of the estimated growth rate for three pigs (2, 13, and 17) for whom there were no weight estimates after November 1st. Here, the model is drawing power from the other pigs to help extrapolate the growth curves for these three pigs, but pig-specific details remain, with the posterior distribution for pig 17 being much more diffuse (wider) than for either pigs 2 or 13, reflecting greater uncertainty for the former animal.
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Mid terms are November 3rd 2026. Go register voters.
@usrbinr.bsky.social
about 19 hours ago
I've officially submitted my {contoso} package to CRAN! Soon I will soon find out how much blood, sweat and tears is required to pass Its a great dataset if you are curious about business - options available from 10k to 100M rows with @duckdb.org #rstats #databs usrbinr.github.io/contoso/

usrbinr.github.io

contoso

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Daniel Falbel
@dfalbel.bsky.social
about 10 hours ago
luz v0.5.1 is now on #rstats CRAN. Just a small bug fix related to forwarding `predict` parameters to the model. I also added a new Variational Autoencoder example to our examples gallery: mlverse.github.io/luz/art….

mlverse.github.io

Examples

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Simon P. Couch
@simonpcouch.com
1 day ago
I'll be keynoting at R/Pharma a week from today! The conference is free and virtual. I'll be focused on the mundane use cases of LLMs for wrangling data with #rstats, and the content should feel applicable for folks outside of pharma—come through. :) Register: events.zoom.us/ev/Ai-geyS63...
A title slide reading "Practical AI for data science" and my name and title.
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OpenForest4D
@openforest4d.bsky.social
about 7 hours ago
Discover how #lidar reveals the size & location of individual Ponderosa Pines at Sunset Crater, AZ! Using advanced algorithms in lidR, we map tree height and crowns in stunning detail, informing forest ecology and management. openforest4d.org/tree-ide…. #Forestry #RemoteSensing #RStats
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Amstat-American Statistical Association
@amstatnews.bsky.social
about 9 hours ago
Could someone be poisoning your kids’ Halloween candy? In this episode of "Stats + Stories," Joel Best joins @johnbailer.bsky.social and @rompenni.bsky.social to unpack this enduring Halloween myth—and how bad stats can fuel social fears. tinyurl.com/ntjdhk54 #STATSSKY
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terence
@tterence.bsky.social
2 days ago
Forest canopy height of Yosemite National Park. #rayshader adventures, an #rstats tale
A visualisation of Yosemite National Park's forest canopy height
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University of Waterloo School of Optometry & Vision Science
@uwoptometry.bsky.social
about 6 hours ago
Researchers at the University of Waterloo School of Optometry & Vision Science/Waterloo Eye Institute have developed a new tool, the Waterloo Differential Acuity Test (WatDAT), that helps detect vision problems in children as young as 18 months. uwaterloo.ca/news/media/b...
A better vision test for toddlers | Waterloo News

uwaterloo.ca

A better vision test for toddlers | Waterloo News

The development of a new tool for testing the eyesight of children under three could mean more children receive treatment for vision difficulties earlier, leading to positive effects on learning and d...

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