Principal component analysis (PCA) is a multivariate technique that analyzes a data table in
which observations are described by several inter‐correlated quantitative dependent …

An overall theme in this book is integration—integrating the products and the processes.
When conducting the literature review of our own in designing this textbook, we recognized …

Crop pathogens and pests reduce the yield and quality of agricultural production. They
cause substantial economic losses and reduce food security at household, national and …

quanteda is an R package providing a comprehensive workflow and toolkit for natural
language processing tasks such as corpus management, tokenization, analysis, and …

Background The analysis of microbial communities through DNA sequencing brings many
challenges: the integration of different types of data with methods from ecology, genetics …

Throughout the social, medical and other sciences the importance of understanding
complex hierarchical data structures is well understood. Multilevel modelling is now the …

Course Description. The course discusses the analysis of multivariate data. Multivariate
distributions and inference about means are considered. Techniques like principal …

The use of some multiple-sequence alignments in phylogenetic analysis, particularly those
that are not very well conserved, requires the elimination of poorly aligned positions and …

The success of the first edition of Generalized Linear Models led to the updated Second
Edition, which continues to provide a definitive unified, treatment of methods for the analysis …

As a result of an error during film output, Table 1 was published with some symbols missing.
The correct version can be found at http://www. sanger. ac. uk and is reproduced again here …