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Extreme Value Analysis in Julia with Extremes.jl | Gabriel Gobeil | JuliaCon 2022
ImplicitDifferentiation.jl: Differentiating Implicit Functions | M. Tarek, G. Dalle | JuliaCon 2022
Using SciML to Predict the Time Evolution of a Complex Network | Andre Macleod | JuliaCon 2022
SpeedyWeather.jl: A 16-bit Weather Model with Machine Learning | Milan Klöwer | JuliaCon 2022
Advances in Transformations and NLP Modeling For InfiniteOpt.jl | Joshua Pulsipher | JuliaCon 2022
Automatic Differentiation for Solid Mechanics in Julia | Andrea Vigliotti | JuliaCon 2022
Unlocking Julia's LLVM JIT Compiler | Prem Chintalapudi | JuliaCon 2022
Julia REPL Mastery Workshop | Miguel Raz Guzmán Macedo | JuliaCon 2022
11 - Calculating DataFrame Column Basic Stats #shorts [Julia and R programming]
Influence of an Atypical Value in the Extreme Wave Analysis Using Non-Stationary GEV Models
A quick way to estimate a general extreme value distribution with R and a GUI, SPY financial data.
SEV Non Stationary Extreme Value
![11 - Calculating DataFrame Column Basic Stats #shorts [Julia and R programming]](https://img.youtube.com/vi/mLDuJiboVfI/hqdefault.jpg)
