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Xinkai Du

@xinkaidu.bsky.social
1.9K followers 514 following 50 posts

PhD in PsychMethods & ClinicalPsych with @sverreuj @SachaEpskamp | Prev @UvAmsterdam @UWaterloo | (Network) Psychometrics; (Intensive) Longitudinal Data; Natural Language Processing; Applied Statistics

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Xinkai Du @xinkaidu.bsky.social · 20/03/2026
Unfortunately, the captions of Figure 2-5's subfigures now have wrong fonts and font sizes. This is an error that they failed to correct during the proof examination, and I have contacted them to fix it. The error is expected to be fixed in three weeks. The fonts in the preprint are fine.
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Xinkai Du @xinkaidu.bsky.social · 20/03/2026
Beyond network comparison, we believe IVPP has broader insights for SEM/psychometrics and meta-analysis in: ▶︎ Determining measurement/parameter invariance ▶︎ Parameter meta-analysis, which reveals parameters that are shared across samples and parameters that are unique to each sample.
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Xinkai Du @xinkaidu.bsky.social · 20/03/2026
2) Previous approaches to compare dynamic networks are only viable when intensive measurements are available. IVPP allows comparing networks when only a few data points (t >= 3) are available per person, a situation common in large-scale longitudinal surveys.
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Xinkai Du @xinkaidu.bsky.social · 20/03/2026
IVPP fills in two essential gaps: 1) Previous approaches to comparing dynamical networks only report the presence/absence of heterogeneity. In contrast, IVPP uncovers edge-level differences, providing a more meaningful network-difference test that reveals the mechanisms of heterogeneity.
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Xinkai Du @xinkaidu.bsky.social · 20/03/2026
Thrilled to share that our work (with Sverre Urnes Johnson and @sachaepskamp.bsky.social) on invariance partial pruning (IVPP), a novel approach to comparing networks in time-series and panel data is now online at Psychological Methods 10.1037/met0000824 (preprint link: 10.31234/osf.io/vb8dz_v2).
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Xinkai Du @xinkaidu.bsky.social · 19/01/2026
The link to the initial study on fit indices and Hu/Bentler cutoffs mentioned earlier: psycnet.apa.org/record/2026-31171-001
psycnet.apa.org
APA PsycNet
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Xinkai Du @xinkaidu.bsky.social · 19/01/2026
In simulations with both synthetic and empirical networks, DFI showed transparent and often superior type I & II error rates vs. Hu/Bentler cutoffs (especially in empirical networks). We argue that the transparency and consistency of DFI provide more reliable model evaluation of network models.
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Xinkai Du @xinkaidu.bsky.social · 19/01/2026
Our recent study on PsychMethod showed that SEM fit indices had desirable sensitivity to the misspecification in (dynamic) networks, yet were also sensitive to sample and model characteristics (e.g., N and network size), so we created DFI for networks to accommodate design-specific characteristics.
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Xinkai Du @xinkaidu.bsky.social · 19/01/2026
My latest work with @sachaepskamp.bsky.social on creating dynamic fit index cutoffs for Gaussian graphical models is now out as a preprint: osf.io/preprints/ps..., accompanied by an R package, netDFI: github.com/xinkaidupsy/....
osf.io
OSF
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Xinkai Du @xinkaidu.bsky.social · 19/01/2026
In simulations with both synthetic and empirical networks, DFI showed transparent and often superior type I & II error rates compared to conventional cutoffs (especially in empirical networks).
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Xinkai Du @xinkaidu.bsky.social · 19/01/2026
Our latest work on PsychMethod showed that SEM fit indices had desirable sensitivity to the misspecification in (dynamic) networks, yet were also sensitive to sample and model characteristics (e.g., N and network size), so we created DFI for networks to accommodate design-specific characteristics.
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Xinkai Du @xinkaidu.bsky.social · 06/10/2025
Day 1 at Stanford and officially started my 4-month US visit in this special time. Amazed by the beautiful campus.
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Xinkai Du @xinkaidu.bsky.social · 23/08/2025
Adopt Registered Reports at Psychological Methods - Sign the Petition! chng.it/8h2KXXR4jk
chng.it
Sign the Petition
Adopt Registered Reports at Psychological Methods
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Xinkai Du @xinkaidu.bsky.social · 12/07/2025
Currently visiting Dr. Johnny Zhang in Notre Dame and excited to learn about his approaches combining CS and psychometrics. Had a wonderful encounter with a deer on the way to campus. :)
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Marcelo Mattar @marcelomattar.bsky.social · 02/07/2025
Thrilled to see our TinyRNN paper in @nature! We show how tiny RNNs predict choices of individual subjects accurately while staying fully interpretable. This approach can transform how we model cognitive processes in both healthy and disordered decisions. doi.org/10.1038/s415...
doi.org
Discovering cognitive strategies with tiny recurrent neural networks - Nature
Modelling biological decision-making with tiny recurrent neural networks enables more accurate predictions of animal choices than classical cognitive models and offers insights into the underlying cog...
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Xinkai Du @xinkaidu.bsky.social · 26/06/2025
@apajournals.bsky.social
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Sacha Epskamp @sachaepskamp.bsky.social · 26/06/2025
Happy to share that our article, led by @xinkaidu.bsky.social, on confirmatory network modeling has been published in Psychological Methods! psycnet.apa.org/record/2026-...
psycnet.apa.org
APA PsycNet
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Xinkai Du @xinkaidu.bsky.social · 26/06/2025
Thrilled to share that this paper has now been published on Psychological Methods. See 🧵 below for an intro & shinyapp to view the results, as well as non-paywalled version. dx.doi.org/10.1037/met0...
dx.doi.org
APA PsycNet
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Julia M. Rohrer @dingdingpeng.the100.ci · 02/06/2025
Some papers are really good because they make just one point, but they make it really clearly — such as “Statistical Control Requires Causal Justification” journals.sagepub.com/doi/10.1177/...
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Xinkai Du @xinkaidu.bsky.social · 26/05/2025
The method works both for panel and n=1 data. By enabling researchers to statistically compare networks across groups/individuals, we hope the method opens new avenues for testing genetic influences, developmental theories, treatment mechanisms, and cross-cultural differences.
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Xinkai Du @xinkaidu.bsky.social · 26/05/2025
I planned to present this method at #SAA2025. Unfortunately I could not make it due to an unforeseen cold. Hope you enjoy the discussion and stay safe and healthy!
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Xinkai Du @xinkaidu.bsky.social · 26/05/2025
The paper also comes with a brief tutorial on the usage of the package
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Xinkai Du @xinkaidu.bsky.social · 26/05/2025
We have implemented IVPP in an R package under the same name: github.com/xinkaidupsy/...
github.com
GitHub - xinkaidupsy/IVPP
Contribute to xinkaidupsy/IVPP development by creating an account on GitHub.
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Xinkai Du @xinkaidu.bsky.social · 26/05/2025
Second, the method allows the comparison of networks when only a few data points (t = 3 or more) are available per person, a situation that is very common in large-scale longitudinal surveys.
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Xinkai Du @xinkaidu.bsky.social · 26/05/2025
In contrast, IVPP uncovers edge-level differences through a novel algorithm we present, termed partial pruning, directly constructing the distinct networks of each group/individual. We believe it provides a more meaningful network difference test that reveals the mechanisms underlying heterogeneity.
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Xinkai Du @xinkaidu.bsky.social · 26/05/2025
IVPP fills in two essential gaps in the literature: First, previous approaches to comparing dynamical networks unfortunately only report the presence/absence of heterogeneity, and are only viable when intensive measurements are available.
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Xinkai Du @xinkaidu.bsky.social · 26/05/2025
The three-month research visit with @sachaepskamp.bsky.social at NUS was a great memory, and even more excited with research output. Excited to share a novel approach to compare networks models in time-series and panel data, which we term invariance partial pruning (IVPP). osf.io/vb8dz_v1
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Hu Chuan-Peng is on paternity leave @hcp4715.bsky.social · 14/02/2025
🥳thrilled that our dockerHDDM tutorial paper, after many years's work was published in my dream journal AMPPS of @psychscience.bsky.social 🤩 👇 doi.org/10.1177/2515.... The image's been downloaded 10K+⏬ docker Hub Such a pleasure to work w/ Wanke, Ru Yuan, Haiyang & member of HDDM/HSSM team!
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Eiko Fried @eikofried.bsky.social · 13/02/2025
1/3 Tutorial on exploring ecological momentary assessment data is online at AMPPS, with: - Accessible ways to visualize data for better understanding - Models to get some first insights - Further reading boxes for more advanced topics - Reproducible pipeline you can run over your own data
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René Freichel @renefreichel.bsky.social · 07/02/2025
Check out this important methodological validation study of SEM fit indices for (confirmatory) network modeling. Led by @xinkaidu.bsky.social!
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Xinkai Du @xinkaidu.bsky.social · 07/02/2025
Thank the collaborators for the continuous support and contribution! @noraskjerdingstad.bsky.social @renefreichel.bsky.social @omidvebrahimi.bsky.social Ria Hoekstra @sachaepskamp.bsky.social
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Xinkai Du @xinkaidu.bsky.social · 07/02/2025
The Shiny app allows users to view the results interactively, as well as checking the rejection rates of different cutoff values they choose by themselves
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Xinkai Du @xinkaidu.bsky.social · 07/02/2025
2. Fit indices were sensitive to mis-defined confirmatory network structures and non-stationarity. 3. Conventional cutoffs were convenient assessment criteria and generally performed well, albeit stricter cutoffs might be needed for hypothesis testing and replication studies
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Xinkai Du @xinkaidu.bsky.social · 07/02/2025
1. Although most network studies are exploratory so far, confirmatory network analysis has been entirely feasible. It is also often neglected that in longitudinal settings, exploratory network models are in-fact semi-confirmatory for the stationarity assumption they rely on.
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Xinkai Du @xinkaidu.bsky.social · 07/02/2025
Preprint on using SEM fit indices & conventional cutoffs in confirmatory network analysis updated! Now with a Shiny app for results display. PsyArXiv: osf.io/preprints/ps... RG: www.researchgate.net/publication/... Shiny app: github.com/xinkaidupsy/... See 🧵 for the summary of results
researchgate.net
(PDF) Moving from exploratory to confirmatory network analysis: An evaluation of SEM fit indices and cutoff values in network psychometrics
PDF | Network models are well-suited for phenomena detection, and most empirical network studies have been exploratory so far. Yet, due to the close... | Find, read and cite all the research you need ...
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Charles Driver @charlesdriver.bsky.social · 15/01/2025
Looking for a longitudinal (ideally intensive) dataset where missing data is expected to be problematic, i.e. MNAR, anyone have pointers to good examples?
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ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 03/01/2025
link 📈🤖 Penalized weighted GEEs for high-dimensional longitudinal data with informative cluter size (Ma, Wang, Jiang) High-dimensional longitudinal data have become increasingly prevalent in recent studies, and penalized generalized estimating equations (GEEs) are often used to model such data. H
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ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 03/01/2025
link 📈🤖 Penalized Quasi-likelihood for High-dimensional Longitudinal Data via Within-cluster Resampling (Ma, Wang, Jiang) The generalized estimating equation (GEE) method is a popular tool for longitudinal data analysis. However, GEE produces biased estimates when the outcome of interest is assoc
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Paolo Crosetto @paolocrosetto.bsky.social · 20/12/2024
I was today years old when I found this gem of a web app to explore 2500+ color palettes for R: r-graph-gallery.com/color-palett...
r-graph-gallery.com
R Color Palette Finder
The ultimate tool for finding the perfect color palette for data visualization with R and paletteer. Explore over 2000 palettes, see them in action on various charts, simulate color blindness, and exp...
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Nick Clark 💹 @njclark.bsky.social · 13/12/2024
A real pleasure to run two workshops on time series and forecasting to wrap up #ESAus2024. Great to see so many wonderful scientists doing temporal ecology in Australia. Hopefully my {mvgam} software can help with some of this: youtube.com/playlist?lis...
youtube.com
Time series in R and Stan using the mvgam package - YouTube
This series of webinars introduces the mvgam R package, which can fit State-Space Dynamic Generalized Additive Models using Bayesian inference to time series...
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PsyArXivBot @psyarxivbot.bsky.social · 10/12/2024
Conspiracy theories and violent extremism: osf.io/emgjf
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Nico Krämer @pnkraemer.bsky.social · 09/12/2024
🛩️ On my way to #NeurIPS2024 and excited to chat about (ML applications of) linear algebra, differentiable programming, and probabilistic numerics! Feel free to DM if you’d like to meet up, hang out, and/or discuss any of these topics 😊 (Where to find me & paper info? -> Thread)
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Xinkai Du @xinkaidu.bsky.social · 26/11/2024
Network psychometrics starter pack go.bsky.app/8ccFL3W
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PsyArXivBot @psyarxivbot.bsky.social · 25/11/2024
Evaluating General Network Scoring Methods as Alternatives to Traditional Factor Scoring Methods: osf.io/s3re6
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Xinkai Du @xinkaidu.bsky.social · 25/11/2024
Definitely!
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Xinkai Du @xinkaidu.bsky.social · 20/11/2024
🤔
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Kristoffer Magnusson @rpsychologist.com · 18/11/2024
Working on some network stuff. It's oddly satisfying going from this network spaghetti to something a bit more organized
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Xinkai Du @xinkaidu.bsky.social · 18/11/2024
Working on this at UiO and would love to be added :)
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Ian Hussey @ianhussey.mmmdata.io · 15/11/2024
FYI Lisa has a multi week course on this recorded. I attended the first week or two and it was great, I just didn’t have the time to continue with it. youtu.be/XiM4jFJ6ocY?...
youtu.be
Intro to R Packages: Setting Up a Package Project (Week 1)
YouTube video by Lisa DeBruine
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Xinkai Du @xinkaidu.bsky.social · 15/11/2024
Haha I think after opening the pack, just click the three dots on top right. Then select edit, you will go through the creation process again. There you can add people
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