For convenience I have consolidated my new Python Jupyter notebooks for ALSM into my legacy R package. The aim is this update is the same, to teach you how to do applied statistics through the ALSM text and using Python coding practices that you can adopt in your professional life.

I moved from data science to data engineering, and with that from R to Python. When a machine learning opportunity arose to investigate non-linear models, I was reminded I am not versed in doing statistics in Python, even though I know most of the algorithms used in my R code should be available in Python standard packages. Also, I do not have an intuition when it comes to which packages to use or how to manipulate Pandas dataframes in the ways I did R or do with Spark, today. This was a very useful exercise in the end!

I hope you enjoy these notebooks. I may finish chapter 13 and do 14. I have no intention, at this time, to work on the 2nd half of the book, focused on design of experiments. I welcome any contributions from the community; send me those pull requests!

This R package provides data and code for all of the tables and figures, to the extent I've completed them. Nearly all examples are provided, save for some advanced materials I wasn't prepared for some years ago when I coded this (e.g., neural networks). These will be updated in later versions of this package.

Vignettes are complete, but the code base will require cleanup. Some package dependencies will be removed, such as rcmdr. Lattice plots may be converted to ggplot alternatives. Milestones will be set to track further inclusion of chapters beyond 18. Demos will be generated from the vignette's code script directly. This will maintain consistency between the two modes of presentation. Demos are there for the interested user; they're not the recommended mode of learning.

For original data files, visit author's book site: https://netfiles.umn.edu/users/nacht001/www/nachtsheim/5th/

The HTML output can be viewed at my RPubs: https://rpubs.com/bryangoodrich