Technical and Programming

Using Julia and Turing to fit compartmental models.

Representing 3D Structure in R (Structural Biology with r3dmol).

Exploring the concentration of PFOA and PFOS in the drinking water.

Moving to Quarto from Distill

A Bayesian reanalysis of estimates for the Pfizer Vaccine candidate for children under five years old. Frequentist statistics say it fails while Bayes would indicate that it should be approved.

Investigating how well Waste Water predicts COVID-19 cases in North Carolina.

Can Bayesian analysis be used to understand the impact of a treatment even though the frequentist results are not significant?

Here looking at the differences in traditional linear contrasts versus Bayesian Hypothesis testing.

How long someone has detectable neutralizing antibodies after vaccination is important in understanding the impact of vaccination on disease transmission. In this post I step through several different models and re-examine data from a prior paper.

Exploring the impact of contact rate and virulence and their impact on the basic reproduction number of a pathogen.

Functions and settings in your Rprofile can be dangerous for reproducibility, but can afford some nice workflow tools.

Are the technical challenges which are judged blindly good indcators of if a baker will win overall?

Comparing crude mortality rates across NC during the COVID-19 pandemic shows differences, but fails to capture the nuance of potential sources of bias.

Using Bayesian Structural Times Series to estimate when some North Carolina counties will be vaccinated to a sufficient number.

This post discuses using the ShinyProxy framework to serve static html sites. These products could be generated from single R Markdown documents to entire websites. Serving these items in containers gives you all the benefits of containerising your work along with the ability to authenticate through ShinyProxy if desired.

In this post I review how to build a compartmental model using the Stan probabilistic computing language. This is based largely by the case study, Bayesian workflow for disease transmission modeling in Stan which has been expanded to include a second compartment for exposed individuals as well as utilise case incidence data rather than prevalence.

Super-spreading events can be characterised by a single case spreading to a larger than expected number of people. This phenomenon can be well-represented by a negative binomial distribution versus a standard Poisson distribution. In this post I review the overdispersion factor and how it can be parameterised in a model.

Using Stan for optimization.

This post explores using tools to summarise curves rather than fixed time summary methods. This includes using odin and ggdist to explore the risk of underestimating epidemic curves.

Use Julia and R to run agent based models in Julia and visualise them in R.

Here I explore the implications of different levels of sensitivity and specificity in a Bayesian framework. All of this work is based on Gelman and Carpenter.

In the post I explore the potential growth rate of Covid-19 to Forsyth County, NC. This also includes looking at the kind of load that this virus could place on our existing healthcare systems. I strongly advocate for acting to delay to flood of potential community acquired infections.

In this I detail the process for getting a working instance of Apache Airflow on Windows Linux Subsystem. This is a combination of several different posts spread across the internet. Apache Airflow is an exceptional program for scheduling and running tasks.

A preview of some of the items that I will try to write about in 2020.

In a previous blog post I looked at approval ratings. Now that impeach is the topic of the day, I think it would be wise to try the same methodology with the public opinion surrounding impeachment. While the data are much more sparse, it will be fun to examine.

Given the current controversy regarding President Trump, let’s use a state-space Bayesian model to see what his approval rating currently is. As more surveys go into the field this will change, but let’s just look now.

Often times when doing an analysis, it is important to put the results in the context of the loss. For example, a small effect that is cheaply implemented might be the best use of resources. Using Bayesian modeling and loss functions we can better assess the impact and provide better information for decision-making when it comes to allocation of scarce resources (especially in the world of small effect sizes).

Some ruminations about the legacy of Apollo and doing things when failure isn’t an option.

Using AWK to parse court calendars

Having a defined project workflow is important for many reasons. Consistency of design allows for easier sharing (you or other collaborators don’t have to look for things) and reduces some cognitive load by allowing you to focus on content and less on form. This is my lightly opinionated project structure. Of course these fews are ever evolving.

Sometimes instead of accuracy we need to look at different metrics. One such metric is sensitivity, which is a measure of those who are actually targets how many does the model correctly identify. This can be the metric of choice over accuracy when you are dealing with a raw event such as a terrorist attack or even student retention. It is always important to understand what metrics you are optimising your models on.

In this section I replicate some state space poll modeling that James Savage and Peter Ellis used in a few different scenarios. State space modeling provides a great way to model times series effects when the data are collected at irregular intervals (e.g. opinion polling).

In this post I explore a potential outcomes to the composition of the Winston-Salem city council.

Exploring the implicates of omited variables in analysis.

Using fake data simulations to understand the our MRP model.

Metropolis Hasting samplers are typically slow in R because of inability to parallelise or vectorise operations. The Rcpp package allows a way to use C++ to conduct these MCMC operations at a much greater speed. This post explores how one would do this, achieving a >20x speed up.

This is a quick overview of a trick to add LaTex in ggplot2.

This post explores MRP using brms and tidyverse modeling.

The purpose of this post is to replicate the examples in the gsynth package for synthetic controls. This is a methodology for causal inference especially at the state level.

This is just a quick reproduction of the items discussed in the hts package. This allows for hierarchical time series which is an important feature when looking at data that take a hierarchical format like counties within a state or precincts within counties within states.

Looking at a blog post that Andrew Gelman posted on fake data simulations and HLM. The power of fake data simulations is that it really makes you think twice about what kind of effect for which you are looking as well as the power of your research design to detect it. This illustrates a really good practice for anyone looking to do this kind of analysis.

Network analysis provides an way to analyse the interconnectedness of different networks. This can provide insight into social networks, interconnected groups of text, tweets, etc. Visualisations help to show these relationships but also some numeric values to quantify them.

Exploring the examples in Kleiber and Zeileis’ Applied Economics in R

Using Fourier Transform as coefficients in short time series data helps with prediction.

Exploring the concept of developing internal APIs. An API could also be an R package that can be used by people in your organisation to more easily connect to common data sources. This is a good example of some internal tooling that can make data access easier.

Item Response Theory (IRT) is a method by which item difficulty is assessed and used to measure latent factors. Classical test theory has a shortcoming where the test-taker’s ability and the difficulty of the item cannot be separated. Thus there is a question of universalisability outside of the instrument. Additionally, the models make some assumptions that mathematically may not be justified. In come IRT which handles some of these issues.

Let’s examine some of the functions inside for forecast

This post explores how to see opportunities to make your code run faster.

Exploring the bsts package and what it provides for Bayesian structural time series modeling

ggrough is a great package that can be used to make graphs that look hand-drawn. This can be a great aesthetic choice when giving presentations and making handouts.

“Exploring the power of gghighlight package to automatically highlight charts”

An example of the value suppressing uncertainty scale. Great uses include forecast uncertainity.