There is no single place for R books on SpringerLink, thus you need to experiment a bit using different search terms, e.g. data science, econometrics, statistics,...
It is just important to *not* tick the field "Include Preview-Only content"
@gerald_leppert I don't know if this is relevant, but these guys als have gone open access:
In my opinion, #R is very suitable for #MachineLearning. With R, machine learning can be easily integrated into usual #rstats data analysis workflows. #RPackages provide access to virtually all relevant machine learning algorithms like #NeuralNetworks, Support Vector machines (#SVM), #RandomForests, Extreme Gradient Boosting (#XGBoost), #WEKA algorithms, etc.
Does anyone of the @rstats group have further recommendations?
See reply for sources: 4 books on machine learning.
Dinov, I.D. (2018): Data Science and Predictive Analytics: Biomedical and Health Applications using R. https://link.springer.com/book/10.1007/978-3-319-72347-1
Irizarry, R.A. (2019): Introduction to Data Science. https://rafalab.github.io/dsbook/
James, G. et al. (2014): An Introduction to Statistical Learning. With Applications in R. https://link.springer.com/book/10.1007/978-1-4614-7138-7
Lantz, B. (2013): Machine Learning with R. https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781788295864
LightGBM is simpler in R than it is in Python, and it works pretty well. Caret is miles better than other options in Python AFAICT.
BSTS _only_ exists in R & C++.
Working with anything that doesn't fit easily into data.frames/tables, it's just not well suited because of the issues with speed, and R is fastest with matrix & data.frame calculations.
NNs & Keras etc, it's better working in Python.