R is a language and environment for data analysis and graphics. It may be
considered an implementation of S, an award-winning language initially developed
at Bell Laboratories since the late 1970s. The R project was initiated
by Robert Gentleman and Ross Ihaka at the University of Auckland, New
Zealand, in the early 1990s, and has been developed by an international team
Here is the first book on applied econometrics using the R system for statistical computing and graphics. It presents hands-on examples for a wide range of models, from classical linear regression models for cross-section to recent semiparametric extensions.
Historically, econometricians have favored other computing environments,
some of which have fallen by the wayside, and also a variety of packages with
canned routines. We believe that R has great potential in econometrics, both
for research and for teaching. There are at least three reasons for this: (1) R
is mostly platform independent and runs on Microsoft Windows, the Mac
family of operating systems, and various flavors of Unix/Linux, and also on
some more exotic platforms. (2) R is free software that can be downloaded
and installed at no cost from a family of mirror sites around the globe, the
Comprehensive R Archive Network (CRAN); hence students can easily install
it on their own machines. (3) R is open-source software, so that the full source
code is available and can be inspected to understand what it really does,
learn from it, and modify and extend it. We also like to think that platform
independence and the open-source philosophy make R an ideal environment
for reproducible econometric research.
Probabilistic Databases (Synthesis Lectures on Data Management)
Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of...
Probability and Algorithms
Some of the hardest computational problems have been successfully attacked through the use of probabilistic algorithms, which have an element of randomness to them. Concepts from the field of probability are also increasingly useful in analyzing the performance of algorithms, broadening our understanding beyond that provided by the...
Python Programming: An Introduction to Computer Science
This is the second edition of John Zelle's Python Programming, updated for Python 3. This book is designed to be used as the primary textbook in a college-level first course in computing. It takes a fairly traditional approach, emphasizing problem solving, design, and programming as the core skills of computer science. However,...
Applied Statistics: Theory and Problem Solutions with R
Instructs readers on how to use methods of statistics and experimental design with R software
Applied statistics covers both the theory and the application of modern statistical and mathematical modelling techniques to applied problems in industry, public services, commerce, and research. It proceeds...