Discover and read the best of Twitter Threads about #stan
Most recents (6)
New paper for ‘ibis.iSDM’ now published in Ecological Informatics. Here I present a new R-package that allows the integration of different datatypes in SDMs. But it can do much more than that... #sdm #biodiversity #conservation
doi.org/10.1016/j.ecoi…
👇(1/8)
doi.org/10.1016/j.ecoi…
👇(1/8)
So one might ask why another R-package for correlative SDMs, and I generally agree. The specific role of the package for me and our group is to a) support all types of integration originally highlighted by Fletcher et al. and more (doi.org/10.1002/ecy.27…, @FletcherEcology) (2/8)
..., b) propose a modelling framework founded in Poisson Process Models and support Bayesian SDMs, c) enable easy construction of scenarios and projections and d) have a modular coding framework that can be easily updated with further functionalities (3/8)
1/ Bayesian inference is a powerful statistical framework that allows us to estimate the probability distribution of parameters based on data and prior knowledge. And R has a variety of packages for implementing Bayesian analysis! #rstats #datascience #Bayesian
1) Wherever I look these days I see #occult symbols and imagery. Was in Hyde Park yesterday and saw this bus with a big ad for #WitchesMovie. 
2) There was this ad for #HamiltonMusical nearby in Elizabeth Street. Not about witchcraft, sure. But it still featured the star motif. This is basically an inverted pentagram. #symbolism #Sydney
3) This was display in boutique on Elizabeth Street. Has a #Christmas theme and it's not surprising that star (of Bethlehem) symbolism would be part of that. But what I think is interesting is that the stars are more prominent than the tree.
New paper: Tooling-up for infectious disease transmission modelling. The Intro to the #Epidemics special issue on Computational Methods for Modelling an Infectious Disease (COMMAND) doi-org.ez.lshtm.ac.uk/10.1016/j.epid… @GrahamMedley @EleanorMRees @N_R_Waterlow @cmmid_lshtm In the series:
1. Choices and trade-offs in inference with infectious disease models by @sbfnk and A King doi.org/10.1016/j.epid… #IDmodelling #modelchoice
2. Desirable BUGS in models of infectious diseases by @kathmoreilly @drrachellowe and others doi.org/10.1016/j.epid… #JAGS #graphs
How do you test 941 voxels without having to correct for multiple comparisons? 🔥🔥
Build 1⃣ Bayesian multilevel model and estimate 1⃣ multidimensional posterior distribution. No correction needed! (see paper below)🧵
Build 1⃣ Bayesian multilevel model and estimate 1⃣ multidimensional posterior distribution. No correction needed! (see paper below)🧵
Inspired by work with @gangchen6 on Bayesian multilevel models at the ROI level:
link.springer.com/article/10.100…
🧵
link.springer.com/article/10.100…
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Lab effort to apply the framework at the ROI and in a voxelwise manner (just insula for now) to study the effect of controllability on shock-plus-sound stressors:
biorxiv.org/content/10.110…
Looking forward to running whole-brain soon.🧵
biorxiv.org/content/10.110…
Looking forward to running whole-brain soon.🧵
1/7 In 2017, Hedge, Powell, and Sumner showed that robust cognitive tasks are unreliable, which calls into question the use of behavioral tasks for studying individual differences. In this blog post, I show that this conclusion is misguided (haines-lab.com/post/thinking-…)
2/7 Specifically, Hedge et al. found that robust effects such as the Stroop effect have test-retest correlations in the range of .5 to .6 (r = .5 in the plot of their Stroop effects shown here), which severely impacts our ability to rank the performance of individual subjects.
3/7 However, this conclusion is based on a model of behavior does not account for variability at the individual-subject level. While this may not seem very problematic at first glance, the assumption of no measurement error leads to substantially biased inference.
