Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Drumroll please……………….!!!!!
With the addition of these 7 new books, the collection now stands at over 400 entries of (mostly) free R books! Many thanks to Markus Gesmann, Jacobus, Max Cotera, Luis, Olivier Leroy and Gary for their latest contributions.
This a truly a one-of-a-kind resource. I want to give immense thanks to all the authors, contributors and of course, the broader R community. Big Book of R currently gets about 80k visitors per year which is a great estimate to the top quality content. From the humble beginnings of 100 books in August 2020 (which even then was a sizeable collection), it’s grown to a pretty impressive library.
Help me cover the annual costs of hosting Big Book of R and the newsletter! Big Book of R, RScreencasts and OscarBaruffa domains; $50/ Plausible.io privacy-focussed Analytics: $ 100. ConvertKit Mailing service (newsletter, to make sure you get notified of new books): $500.
I’m already putting all previous donations, recent book sales and affiliate sales into the fund and your help will be much appreciated!
As the time of print, I’ve already reached 31% of the goal 
And now, onto the new entries!
Big Data Analytics
by Ulrich Matter
This is the website of the 1st edition of “Big Data Analytics”. The book provides an introduction to Big Data Analytics for academics and practitioners
https://www.bigbookofr.com/big-data#big-data-analytics
Hierarchical Compartmental Reserving Models
by Markus Gesmann, Jake Morris
Hierarchical compartmental reserving models provide a parametric framework for describing aggregate insurance claims processes using differential equations. We discuss how these models can be specified in a fully Bayesian modeling framework to jointly fit paid and outstanding claims development data, taking into account the random nature of claims and underlying latent process parameters. We demonstrate how modelers can utilize their expertise to describe specific development features and incorporate prior knowledge into parameter estimation. We also explore the subtle yet important difference between modeling incremental and cumulative claims payments. Finally, we discuss parameter variation across multiple dimensions and introduce an approach to incorporate market cycle data such as rate changes into the modeling process. Examples and case studies are shown using the probabilistic programming language Stan via the brms package in R.
https://www.bigbookofr.com/field-specific#hierarchical-compartmental-reserving-models
Targeted Learning in R: Causal Data Science with the tlverse Software Ecosystem
by Will Landau
targets has an elaborate structure to support its advanced features while ensuring decent performance. This bookdown site is a design specification to explain the major aspects of the internal architecture, including the data storage model, object oriented design, and orchestration and branching model
https://www.bigbookofr.com/packages#the-targets-r-package-design-specification
R para epidemiología aplicada y salud pública
by Neale Batra, Alex Spina, Paula Bianca Blomquist
EpiRhandbook es un manual de referencia de R aplicado a la epidemiología y la salud pública.
(This is the EpiR Handbook in Spanish)
https://www.bigbookofr.com/espa%C3%B1ol.html#r-para-epidemiolog%C3%ADa-aplicada-y-salud-p%C3%BAblica
Targeted Learning in R: Causal Data Science with the tlverse Software Ecosystem
by Mark van der Laan, Jeremy Coyle, Nima Hejazi, Ivana Malenica, Rachael Phillips, Alan Hubbard
It is a fully reproducible, open-source, electronic handbook for applying Targeted Learning methodology in practice using the software stack provided by the tlverse ecosystem.
Psychometrics in Exercises using R and RStudio
by Anna Brown
Provides a comprehensive set of exercises for practicing all major Psychometric techniques using R and RStudio. The exercises are based on real data from research studies and operational assessments, and provide step-by-step guides that an instructor can use to teach students, or readers can use to learn independently. Each exercise includes a worked example illustrating data analysis steps and teaching how to interpret results and make analysis decisions, and self-test questions that readers can attempt to check own understanding.
https://www.bigbookofr.com/social-science#psychometrics-in-exercises-using-r-and-rstudio
R Bytecode Book
by Mikefc aka coolbutuseless
This is a book about the bytecode which drives the virtual machine at the heart of R code execution. This book represents my current (and still evolving) understanding of bytecode, and I hope to use this understanding to break R in new and exciting ways.
https://www.bigbookofr.com/r-programming#r-bytecode-book
If you want to never miss out on updates to the collection, sign up to the newsletter.
The post Big Book of R at 400 [New milestone!] appeared first on Oscar Baruffa.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.