Optimal Experimental Design with R

Introduction
Experimentation and empirical research
Designing experiments
Some basic definitions
Block designs
About the R-programs

Determining the Minimal Size of an Experiment for Given Precision
Sample Size Determination in Completely Randomised Designs
Introduction
Confidence estimation
Selection procedures
Testing hypotheses
Summary of sample size formulae

Size of Experiments in Analysis of Variance Models
Introduction
One-way layout
Two-way layout
Three-way layout

Sample Size Determination in Model II of Regression Analysis
Introduction
Confidence intervals
Hypothesis testing
Selection procedures

Sequential Designs
Introduction
Wald's sequential likelihood ratio test (SLRT) for one-parametric exponential families
Test about means for unknown variances
Triangular designs
A sequential selection procedure

Construction of Optimal Designs
Constructing Balanced Incomplete Block Designs
Introduction
Basic definitions
Construction of BIBD

Constructing Fractional Factorial Designs
Introduction and basic notations
Factorial designs|basic definitions
Fractional factorials design with two levels of each factor (2^p-k designs)
Fractional factorial designs with three levels of each factor (3^p-k-designs)

Exact Optimal Designs and Sample Sizes in Model I of Regression Analysis
Introduction
Exact Φ-optimal designs
Determining the size of an experiment

Special Designs
Second Order Designs
Central composite designs
Doehlert designs
D-optimum and G-optimum second order designs
Comparing the determinant criterion for some examples

Mixture Designs
Introduction
The simplex lattice designs
Simplex centroid designs
Extreme vertice designs
Augmented designs
Constructing optimal mixture designs with R
An example

Theoretical Background
Non-central distributions
Groups, fields and finite geometries
Difference sets
Hadamard matrices
Existence and non-existence of non-trivial BIBD
Conference matrices
Index