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Focusing on practical applications, this guide teaches scientists and engineers how to leverage R and its packages for solving complex mathematical problems. It includes worked examples and code to illustrate key concepts, covering essential aspects of R programming. Readers will learn to create informative graphs, perform statistical analyses, and fit data to both linear and nonlinear models. The book also delves into Monte Carlo methods, stochastic and deterministic approaches, numerical differentiation and integration, interpolation, curve fitting, and optimization techniques.
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Using R for Numerical Analysis in Science and Engineering, Victor A Bloomfield
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- 2014
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