Gnu Scientific Library

The GNU Scientific Library (GSL) is a free numerical library for C and C++ programmers. It provides over 1,000 routines for solving mathematical problems in science and engineering. Written by the developers of GSL this reference manual is the definitive guide to the library. All the money raised from the sale of this book supports the development of the GNU Scientific Library. This is the third edition of the manual, and corresponds to version 1.12 of the library (updated January 2009).

The GNU Scientific Library (GSL) is a numerical library for C and C++ programmers. It is free software under the GNU General Public License. The library provides a wide range of mathematical routines such as random number generators, special functions and least-squares fitting. There are over 1000 functions in total with an extensive test suite. The complete range of subject areas covered by the library includes, Complex Numbers Roots of Polynomials Special Functions Vectors and Matrices Permutations Sorting BLAS Support Linear Algebra Eigensystems Fast Fourier Transforms Quadrature Random Numbers Quasi-Random Sequences Random Distributions Statistics Histograms N-Tuples Monte Carlo Integration Simulated Annealing Differential Equations Interpolation Numerical Differentiation Chebyshev Approximation Series Acceleration Discrete Hankel Transforms Root-Finding Minimization Least-Squares Fitting Physical Constants IEEE Floating-Point Discrete Wavelet Transforms Basis splines Running Statistics Sparse Matrices and Linear Algebra Unlike the licenses of proprietary numerical libraries the license of GSL does not restrict scientific cooperation. It allows you to share your programs freely with others.

Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results. Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date. Modeling with Data will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.

Introduction to Computational Modeling Using C and Open-Source Tools presents the fundamental principles of computational models from a computer science perspective. It explains how to implement these models using the C programming language. The software tools used in the book include the Gnu Scientific Library (GSL), which is a free software libra

A brief introduction to scientific computing with GNU Octave. Designed as a textbook supplement for freshman and sophomore level linear algebra and calculus students.

Rcpp is the glue that binds the power and versatility of R with the speed and efficiency of C++. With Rcpp, the transfer of data between R and C++ is nearly seamless, and high-performance statistical computing is finally accessible to most R users. Rcpp should be part of every statistician's toolbox. -- Michael Braun, MIT Sloan School of Management "Seamless R and C++ integration with Rcpp" is simply a wonderful book. For anyone who uses C/C++ and R, it is an indispensable resource. The writing is outstanding. A huge bonus is the section on applications. This section covers the matrix packages Armadillo and Eigen and the GNU Scientific Library as well as RInside which enables you to use R inside C++. These applications are what most of us need to know to really do scientific programming with R and C++. I love this book. -- Robert McCulloch, University of Chicago Booth School of Business Rcpp is now considered an essential package for anybody doing serious computational research using R. Dirk's book is an excellent companion and takes the reader from a gentle introduction to more advanced applications via numerous examples and efficiency enhancing gems. The book is packed with all you might have ever wanted to know about Rcpp, its cousins (RcppArmadillo, RcppEigen .etc.), modules, package development and sugar. Overall, this book is a must-have on your shelf. -- Sanjog Misra, UCLA Anderson School of Management The Rcpp package represents a major leap forward for scientific computations with R. With very few lines of C++ code, one has R's data structures readily at hand for further computations in C++. Hence, high-level numerical programming can be made in C++ almost as easily as in R, but often with a substantial speed gain. Dirk is a crucial person in these developments, and his book takes the reader from the first fragile steps on to using the full Rcpp machinery. A very recommended book! -- Søren Højsgaard, Department of Mathematical Sciences, Aalborg University, Denmark "Seamless R and C ++ Integration with Rcpp" provides the first comprehensive introduction to Rcpp. Rcpp has become the most widely-used language extension for R, and is deployed by over one-hundred different CRAN and BioConductor packages. Rcpp permits users to pass scalars, vectors, matrices, list or entire R objects back and forth between R and C++ with ease. This brings the depth of the R analysis framework together with the power, speed, and efficiency of C++. Dirk Eddelbuettel has been a contributor to CRAN for over a decade and maintains around twenty packages. He is the Debian/Ubuntu maintainer for R and other quantitative software, edits the CRAN Task Views for Finance and High-Performance Computing, is a co-founder of the annual R/Finance conference, and an editor of the Journal of Statistical Software. He holds a Ph.D. in Mathematical Economics from EHESS (Paris), and works in Chicago as a Senior Quantitative Analyst.

A crisp and sparkling blend of cognitive science and human behavior that offers meaningful and attainable pathways towards becoming our best selves. Why do we feel like in order to be productive, happy, or good, we must sacrifice everything else? Is it possible to feel all three at once? Without even knowing it, we’re doing things everyday to sabotage ourselves and our societies, habits that prevent us from optimizing long term happiness. Where most books imagine solutions that, when enacted, fail to fundamentally improve our lives, Jim Davies grounds his research in cognitive science to show you not only what works, but how much it works. Being the Person Your Dog Thinks You Are shows us how we can use science to become our best selves, using resources we already have within our own brains. Davies's book challenges and inspires us to approach the big picture while also staying mindful of the everyday details in real life. Davies proves why multitasking is bad for you, when a little unmindfulness can be good for you, how to best justify which charities to donate to, and how to hack your brain. The most surprising truth Davies offers us spreads across these pages like wildfire: you too can lead an optimally good life, not through uprooting your life from the ground up, but from adapting your mentality to your given present. A better life doesn’t need to look like a massive change—like our beloved dogs who already view us as our best selves, it’s already much closer than you think.