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A Gentle Introduction: The Gaussian Copula

Copulas in Stan: Episode II

stan

copulas

copulas in stan

This post continues the series on copulas in Stan by introducing the Gaussian copula, discussing its properties, applications, and providing examples of how to implement it in Stan.

Brynjólfur Gauti Guðrúnar Jónsson

If It Bleeds, We Can Kill It

Copulas in Stan: Episode 1

stan

copulas

copulas in stan

This is the first post in what’s going to be a series on using Copulas in Stan. Each post is going to be short to keep me from postponing writing them. In this post I lightly introduce the series and give a quick primer on copulas.

Brynjólfur Gauti Guðrúnar Jónsson

Applying a Gaussian AR(1) Copula to Generalized Extreme Value Margins

A simulation study of the differences between AR(1) copula and i.i.d copula

english

R

phd

stan

bayesian

spatial statistics

copulas

Another step in my PhD studies is to apply multivariate copulas to data with Generalized Extreme Value marginal distributions. This is important to enable us to model dependence on the data leve, as opposed to the latent (parameter) level.

Brynjólfur Gauti Guðrúnar Jónsson

Spatially Dependent Generalized Extreme Value Parameters

Using Stan to fit a Generalized Extreme Value Distribution with spatially dependent parameters

english

R

phd

stan

bayesian

spatial statistics

hierarchical modeling

Note: I’m still working on this post, but I thought I might put it out there while I work on it.

Brynjólfur Gauti Guðrúnar Jónsson

Making interactive animations with ggplotly

Simple examples of how to use the frame aesthetic in ggplotly() for easy animations

english

R

ggplot

plotly

I’ve recently been making a lot of plotly graphs for my Icelandic website metill.is, where I look at official data on current topics. Since it is so simple to make animations with ggplotly I thought I might write a short tutorial on it.

Brynjólfur Gauti Guðrúnar Jónsson

Fetching FTP data from the Ceda Archives

Using R and FTP to programmatically fetch a large amount of data for processing

english

R

phd

big data

scraping

For my PhD modeling I needed to fetch a large amount of data from the CEDA Archive, specifically I use hourly precipitation projections from UKCP Local Projections on a 5km grid over the UK for 1980-2080. The hourly precipitation projections are stored in 720 files that are all approximately 120mb to 130mb. Here I write out my processing in case someone needs help with doing something similar.

Brynjólfur Gauti Guðrúnar Jónsson