Introduction I had wanted to get into R package creation for a while now, but finally got a chance to do so for my lab’s recent Zika paper, Assessing Real-time Zika Risk in the United States. If you’re interested in learning about the content from that paper you can check out the blogpost hosted on the BMC Infectious Diseases blog. I’ve also given an introduction to the package on the rtZIKVrisk github.
Introduction I recently was doing model fitting on a ton of simulations, and needed to figure out a way to speed things up. My first instinct was to get out of the R environment and write CSnippets for the pomp package (more on this in a later blog), or to use RCpp, but I used the profvis package to help diagnose the speed issues, and found a really simple change that can save a ton of time depending on your model.
Introduction This demonstration is part of a requirement for my statistical consulting class at UT Austin. I will go through the basics of bootstrapping time series data using three different resampling methods. Fixed Block Sampling Stationary Block Sampling Model-based resampling Packages Used For this demonstration I will use the following packages: The boot package is the workhorse behind the bootstrapping methods, but the forecast method is used for the time series modeling.