Programming with R Part-5

Bioinformatics and Computational Biology Solutions Using R and Bioconductor
by Robert Gentleman

This volume's coverage is broad and ranges across most of the key capabilities of the Bioconductor project, including importation and preprocessing of high-throughput data from microarray, proteomic, and flow cytometry platforms.
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Statistical Tools for Nonlinear Regression
A Practical Guide with S-PLUS and R Examples
by Sylvie Huet
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Simulation and Inference for Stochastic Differential Equations With R Examples
by Stefano M. Iacus
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Decision Theory Principles and Approaches
by Giovanni Parmigiani
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The Analysis of Gene Expression Data
by Giovanni Parmigiani
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S Programming
by William Venables

A highly recommended book on how to do statistical data analysis using R or S-Plus. In the first chapters it gives an introduction to the S language. Then it covers a wide range of statistical methodology, including linear and generalized linear models, non-linear and smooth regression, tree-based methods, random and mixed effects, exploratory multivariate analysis, classification, survival analysis, time series analysis, spatial statistics, and optimization. The `on-line complements' available at the books homepage provide updates of the book, as well as further details of technical material.
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An R and S Plus Companion to Applied Regression
by John Fox

A companion book to a text or course on applied regression (such as “Applied Regression, Linear Models, and Related Methods” by the same author). It introduces S, and concentrates on how to use linear and generalized-linear models in S while assuming familiarity with the statistical methodology.
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Regression Diagnostics An Introduction
by John Fox
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Statistical Modeling and Analysis for Database Marketing
Effective Techniques for Mining Big Data
by Bruce Ratner
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