Getting started - SAS Engine for Rmarkdown To get started using SAS as your statistical software/data processing “engine” take a look at the following article: http://www.ssc.wisc.edu/~hemken/SASworkshops/Markdown/SASmarkdown.html. Also rea...

Getting started - SAS Engine for Rmarkdown To get started using SAS as your statistical software/data processing “engine” take a look at the following article: http://www.ssc.wisc.edu/~hemken/SASworkshops/Markdown/SASmarkdown.html. Also rea...

We are much better at handling diseases than 30 years ago. For example cancer survival rates are much higher now. The significant portion of this increase can be attributed directly to our ability to detect and diagnose cancer earlier. Also, use of insulin and other drugs to control blood glucose in ...

I had an awesome time at the Enterprise Applications of the R Language (EARL) Conference held in London in September, 2019. EARL reminded me that it is good to keep showing up at conferences. I entered and the first thing I heard was organisers at the table welcoming me “Damian is ...

Have you ever taken a survey at the doctor or for a job interview and wondered what exactly that data was used for? There is a long-standing series of methodologies, many coming from psychology, on how to reliably measure “latent” traits, such as depression or loyalty, from self-report survey data.  ...

In this tutorial, we’ll show how we used clean_anomalies() from the anomalize package to reduce forecast error by 9%. R Packages Covered: anomalize - Time series anomaly detection Cleaning Anomalies to Reduce Forecast Error by 9% We can often...

The easystats project continues to grow, expanding its capabilities and features, and the parameters package 0.2.0 update is now on CRAN. The primary goal of this package is to provide utilities for processing the parameters of various statistical mod...

The easystats project continues to grow, expanding its capabilities and features, and the parameters package 0.2.0 update is now on CRAN. The primary goal of this package is to provide utilities for processing the parameters of various statistical ...

Nuances of Bootstrapping Most applied statisticians and data scientists understand that bootstrapping is a method that mimics repeated sampling by drawing some number of new samples (with replacement) from the original sample in order to perform inference. However, it can be difficult to understand output from the software that carries ...

The easystats project continues to grow, expanding its capabilities and features, and the parameters package 0.2.0 update is now on CRAN. The primary goal of this package is to provide utilities for processing the parameters of various statistical ...

Modular R tools for Bayesian regression are provided by bamlss: From classic MCMC-based GLMs and GAMs to distributional models using the lasso or gradient boosting. Citation Umlauf N, Klein N, Simon T, Zeileis A (2019). “bamlss...

Modular R tools for Bayesian regression are provided by bamlss: From classic MCMC-based GLMs and GAMs to distributional models using the lasso or gradient boosting. Citation Umlauf N, Klein N, Simon T, Zeileis A (2019). “baml...

When you are in 101 of risk management is usual to confuse Bar, VAR and VaR, the first one refers to a place that you should buy (it is a bad idea, do not do it), the second is Vector Autoregressive and the last one Value at Risk, our matter. What ...

In this post I’ll be attempting to leverage the parsnip package in R to run through some straightforward predictive analytics/machine learning. Parsnip provides a flexible and consistent interface to apply common regression and classification algorithms in R. I’ll be working with the Cleveland Clinic Heart Disease dataset ...

I’m proud to announce the release of an R package that has cured one of my own personal itches: pulling and working with USDA data, specifically Quick Stats data from NASS. tidyUSDA is a minimal package for doing just that. The following is cut out from the package vignette, ...