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Spatial Data Science is a book for data scientists with intermediate R knowledge. The book gives a detailed explanation of the core spatial software packages for R: sf for simple feature... Læs mere
Bemærk: Kan ikke leveres før jul.
Bemærk: Kan ikke leveres før jul.
This book discusses advanced topics such as R core programing, object oriented R programing, parallel computing with R, and spatial data types. The author leads... Læs mere
Bemærk: Kan ikke leveres før jul.
A short, accessible workbook detailing how to make the best use of the EQS software, often used in psychological and behavioural research. It has... Læs mere
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This book describes the use of different Mathematical Modeling and Soft Computing techniques used in Epidemiology for experiential research in... Læs mere
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This book takes the reader through the basics of using the R statistical programming environment for data analysis using a series of case studies based around tasks that answer real research questions using publicly available molecular biology datasets.
Bemærk: Kan ikke leveres før jul.
This book takes the reader through the basics of using the R statistical programming environment for data analysis using a series of case studies based around tasks that answer real research questions using publicly available molecular biology datasets.
Bemærk: Kan ikke leveres før jul.
Bemærk: Kan ikke leveres før jul.
S-PLUS is the statistical software of choice for many applied researchers in disciplines ranging from finance to medicine. This book focuses on... Læs mere
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Treats the topics of multiple comparisons, simultaneous and selective inference from avariety of different perspectives. The need for a systematic treatment of the... Læs mere
Bemærk: Kan ikke leveres før jul.
This book provides a broad overview of GAMLSS methodology and how it is implemented in R. It includes a comprehensive collection of real data examples, integrated code, and figures to illustrate the methods, and is supplemented by a website.
Bemærk: Kan ikke leveres før jul.
This book provides a broad overview of GAMLSS methodology and how it is implemented in R. It includes a comprehensive collection of real data examples, integrated code, and figures to illustrate the methods, and is supplemented by a website.