Xinkai Du @xinkaidu.bsky.social · 20/03/2026Unfortunately, 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. 030
Xinkai Du @xinkaidu.bsky.social · 20/03/2026Beyond 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. 130
Xinkai Du @xinkaidu.bsky.social · 20/03/20262) 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. 110
Xinkai Du @xinkaidu.bsky.social · 20/03/2026IVPP 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. 110
Xinkai Du @xinkaidu.bsky.social · 20/03/2026Thrilled 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). 141
Xinkai Du @xinkaidu.bsky.social · 19/01/2026The link to the initial study on fit indices and Hu/Bentler cutoffs mentioned earlier: psycnet.apa.org/record/2026-31171-001psycnet.apa.orgAPA PsycNet 000
Xinkai Du @xinkaidu.bsky.social · 19/01/2026In 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. 100
Xinkai Du @xinkaidu.bsky.social · 19/01/2026Our 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. 100
Xinkai Du @xinkaidu.bsky.social · 19/01/2026My 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.ioOSF 140
Xinkai Du @xinkaidu.bsky.social · 19/01/2026In 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). 000
Xinkai Du @xinkaidu.bsky.social · 19/01/2026Our 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. 100
Xinkai Du @xinkaidu.bsky.social · 06/10/2025Day 1 at Stanford and officially started my 4-month US visit in this special time. Amazed by the beautiful campus. 020
Xinkai Du @xinkaidu.bsky.social · 23/08/2025Adopt Registered Reports at Psychological Methods - Sign the Petition! chng.it/8h2KXXR4jkchng.itSign the PetitionAdopt Registered Reports at Psychological Methods 041
Xinkai Du @xinkaidu.bsky.social · 12/07/2025Currently 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. :) 041
Reposted by Xinkai DuMarcelo Mattar @marcelomattar.bsky.social · 02/07/2025Thrilled 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.orgDiscovering cognitive strategies with tiny recurrent neural networks - NatureModelling 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... 8328140
Reposted by Xinkai DuSacha Epskamp @sachaepskamp.bsky.social · 26/06/2025Happy 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.orgAPA PsycNet 1278
Xinkai Du @xinkaidu.bsky.social · 26/06/2025Thrilled 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.orgAPA PsycNet 1187
Reposted by Xinkai DuJulia M. Rohrer @dingdingpeng.the100.ci · 02/06/2025Some 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/... 710625
Xinkai Du @xinkaidu.bsky.social · 26/05/2025The 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. 000
Xinkai Du @xinkaidu.bsky.social · 26/05/2025I 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! 110
Xinkai Du @xinkaidu.bsky.social · 26/05/2025The paper also comes with a brief tutorial on the usage of the package 100
Xinkai Du @xinkaidu.bsky.social · 26/05/2025We have implemented IVPP in an R package under the same name: github.com/xinkaidupsy/...github.comGitHub - xinkaidupsy/IVPPContribute to xinkaidupsy/IVPP development by creating an account on GitHub. 110
Xinkai Du @xinkaidu.bsky.social · 26/05/2025Second, 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. 100
Xinkai Du @xinkaidu.bsky.social · 26/05/2025In 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. 100
Xinkai Du @xinkaidu.bsky.social · 26/05/2025IVPP 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. 100
Xinkai Du @xinkaidu.bsky.social · 26/05/2025The 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 2101
Reposted by Xinkai DuHu 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! 1168
Reposted by Xinkai DuEiko Fried @eikofried.bsky.social · 13/02/20251/3 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 615477
Reposted by Xinkai DuRené Freichel @renefreichel.bsky.social · 07/02/2025Check out this important methodological validation study of SEM fit indices for (confirmatory) network modeling. Led by @xinkaidu.bsky.social! 062
Xinkai Du @xinkaidu.bsky.social · 07/02/2025Thank the collaborators for the continuous support and contribution! @noraskjerdingstad.bsky.social @renefreichel.bsky.social @omidvebrahimi.bsky.social Ria Hoekstra @sachaepskamp.bsky.social 020
Xinkai Du @xinkaidu.bsky.social · 07/02/2025The Shiny app allows users to view the results interactively, as well as checking the rejection rates of different cutoff values they choose by themselves 100
Xinkai Du @xinkaidu.bsky.social · 07/02/20252. 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 100
Xinkai Du @xinkaidu.bsky.social · 07/02/20251. 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. 100
Xinkai Du @xinkaidu.bsky.social · 07/02/2025Preprint 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 resultsresearchgate.net(PDF) Moving from exploratory to confirmatory network analysis: An evaluation of SEM fit indices and cutoff values in network psychometricsPDF | 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 ... 1145
Reposted by Xinkai DuCharles Driver @charlesdriver.bsky.social · 15/01/2025Looking for a longitudinal (ideally intensive) dataset where missing data is expected to be problematic, i.e. MNAR, anyone have pointers to good examples? 599
Reposted by Xinkai DuArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 03/01/2025link 📈🤖 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 001
Reposted by Xinkai DuArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 03/01/2025link 📈🤖 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 001
Reposted by Xinkai DuPaolo Crosetto @paolocrosetto.bsky.social · 20/12/2024I 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.comR Color Palette FinderThe 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... 825278
Reposted by Xinkai DuNick Clark 💹 @njclark.bsky.social · 13/12/2024A 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.comTime series in R and Stan using the mvgam package - YouTubeThis series of webinars introduces the mvgam R package, which can fit State-Space Dynamic Generalized Additive Models using Bayesian inference to time series... 1507
Reposted by Xinkai DuPsyArXivBot @psyarxivbot.bsky.social · 10/12/2024Conspiracy theories and violent extremism: osf.io/emgjf 001
Reposted by Xinkai DuNico 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) 1113
Xinkai Du @xinkaidu.bsky.social · 26/11/2024Network psychometrics starter pack go.bsky.app/8ccFL3W 010
Reposted by Xinkai DuPsyArXivBot @psyarxivbot.bsky.social · 25/11/2024Evaluating General Network Scoring Methods as Alternatives to Traditional Factor Scoring Methods: osf.io/s3re6 022
Reposted by Xinkai DuKristoffer Magnusson @rpsychologist.com · 18/11/2024Working on some network stuff. It's oddly satisfying going from this network spaghetti to something a bit more organized 53110
Reposted by Xinkai DuIan Hussey @ianhussey.mmmdata.io · 15/11/2024FYI 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.beIntro to R Packages: Setting Up a Package Project (Week 1)YouTube video by Lisa DeBruine 42813
Xinkai Du @xinkaidu.bsky.social · 15/11/2024Haha 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 110