We’re closing on on 400 books in the collection!

A new Economics chapter has been added and some of the books previously in Finance have been moved here.

This post rounds up 7 of the newest additions. Thanks to Lluís Revilla, Mario De Toma and Gary for their submissions:

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Deep R Programming

by Marek Gagolewski

A comprehensive and in-depth introductory course on one of the most popular languages for data science. It equips ambitious students, professionals, and researchers with the knowledge and skills to become independent users of this potent environment so that they can tackle any problem related to data wrangling and analytics, numerical computing, statistics, and machine learning.

https://www.bigbookofr.com/r-programming.html#deep-r-programming

Deep Learning and Scientific Computing with R torch

by Sigrid Keydana

This is a book about torch, the R interface to PyTorch. PyTorch, as of this writing, is one of the major deep-learning and scientific-computing frameworks, widely used across industries and areas of research. With torch, you get to access its rich functionality directly from R, with no need to install, let alone learn, Python.

https://www.bigbookofr.com/machine-learning.html#deep-learning-and-scientific-computing-with-r-torch

Computing Matrix Algebra

by Mario De Toma

“Nobody can be a poet without feeling strong affection for words, at the same time nobody can be serious about data science without becoming close friend to matrices.”

This book is actually a cheat sheet about computing matrix algebra operations such as matrix multiplication, inversion and factorization.

It is written foR (aspiring) data scientists where with “foR” (capital letter R) I mean the side of data science addicted to R and its gorgeous ecosystem especially including Rcpp, RcppArmadillo and RcppEigen.

https://www.bigbookofr.com/field-specific.html#computing-matrix-algebra

Applied Microeconometrics

by Achim Zeileis, Christian Kleiber

This project will gradually turn the course materials for the “Econometrics and Statistics: Microeconometrics” course at Universität Innsbruck into an online book.

The topics covered roughly follow the book Analysis of Microdata by Winkelmann & Boes (2009, Springer-Verlag) and encompass: models for categorical responses (binary, multinomial, ordered), count data, limited dependent variables, and duration models.

https://www.bigbookofr.com/economics.html#applied-microeconometrics-with-r

R for Economic Research

by J. Renato Leripio

Over the past years I’ve received a lot of messages asking what I considered to be the most important subjects one should learn in order to start a career in economic research. R for Economic Research is my contribution to those who have some knowledge of R programming but still lack the necessary tools to carry out professional economic analysis. This is an intermediate-level book where the reader will find shortcuts to start working on a variety of tasks and also valuable references to delve into the details of more complex topics.

https://www.bigbookofr.com/economics.html#r-for-economic-research

Spatial sampling with R

by Dick J. Brus

This book describes and illustrates classical, basic sampling designs for a spatial survey, as well as more recently developed, advanced sampling designs and estimators. Part I of the book is about random sampling designs for estimating a mean, total, or proportion of a population or of several subpopulations. Part II focuses on sampling designs for mapping.

https://www.bigbookofr.com/geospatial.html#spatial-sampling-with-r

Simulation Models of Cultural Evolution in R

by Alex Mesoudi

This book sets out a series of tutorials for modelling cultural evolution in R.

https://www.bigbookofr.com/social-science.html#simulation-models-of-cultural-evolution-in-r

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The post 7 New books added to Big Book of R appeared first on Oscar Baruffa.