infer implements an expressive grammar to perform statistical inference that adheres to the tidyverse design framework (Wickham et al., 2019). Rather than providing methods for specific statistical tests, this package consolidates the principles that are shared among common hypothesis tests and confidence intervals into a set of four main verbs (functions), supplemented with many utilities to visualize and extract value from their outputs.
We present the moderndive R package of datasets and functions for tidyverse-friendly introductory linear regression (Wickham, Averick, et al., 2019). These tools leverage the well-developed tidyverse and broom packages to facilitate 1) working with regression tables that include confidence intervals, 2) accessing regression outputs on an observation level (e.g. fitted/predicted values and residuals), 3) inspecting scalar summaries of regression fit (e.g. R 2 , R 2 adj , and mean squared error), and 4) visualizing parallel slopes regression models using ggplot2-like syntax
His research interests are primarily in image processing, image analysis, computer vision, and engineering education. He has extensive industry experience as a computer hardware design engineer.
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