Event History Analysis with R presents an introduction to survival and event history analysis, with an emphasis on using the R software for implementation of the methods. It focuses on practical applications in the fields of demography, epidemiology, and econometrics. Keeping mathematical details to a minimum, the author covers key topics, including both discrete and continuous time data, parametric proportional hazards, and accelerated failure times.

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A practical guide for handling missing data, Flexible Imputation of Missing Data provides a flexible and accessible framework for multiple imputation along with strategies for obtaining effective solutions to these problems. All of the analyses can be replicated in R using the dedicated package MICE, which was developed by the author.

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R for Statistics explores the use of R for classical statistical analysis. The first half of the book introduces R, data manipulation and visualization, statistical models, graphics, and R programming. The second half presents various statistical analysis techniques using R.

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The R Primer presents concise examples and solutions to R problems frequently encountered by new users of this statistical software. Numerous examples illustrate a specific situation, topic, or problem, including data importing, data management, classical statistical analyses, and high-quality graphics production.

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Also available in eBook.

An R Companion to
Linear Statistical Models

Statistical Computing
in C++ and R

Biostatistics:
A Computing Approach

Analysis of Questionnaire Data with R

Practical Statistical Methods:
A SAS Programming Approach

Applied Operational
Research with SAS

Statistics and Data Analysis
for Microarrays Using R and
Bioconductor, Second Edition

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