that's quite a few things to cover indeed. I am not sure you will find something that is tailored to exactly this list of questions. For the topic of modeling, data normalization, I can recommend that you have a look at recipes and rsample (and more generally into tidymodels). When it comes to neural networks, that is a big topic in itself and depends on your level - one of the most powerful tools out there is the keras library which also has an R interface (https://keras.rstudio.com).

For all points except number four (neural nets), please refer to the chapter on regression in "Introduction to statistical learning with R" by Witten, James, Hastie and Tibshirani. The book is available for free at the following link: http://faculty.marshall.usc.edu/gareth-james/

For shallow neural nets, find a tutorial using R package neuralnet. For deep neural networks, see: