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Exploring Variability in Reflood Simulation Results: an Application of Functional Data Analysis

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Functional programming

In computer science, functional programming is a programming paradigm where programs are constructed by applying and composing functions. It is a declarative programming paradigm in which function definitions are trees of expressions that map values to other values, rather than a sequence of imperative statements which update the running state of the program. In functional programming, functions are treated as first-class citizens, meaning that they can be bound to names (including local identifiers), passed as arguments, and returned from other functions, just as any other data type can.

Genetic variability

Genetic variability is either the presence of, or the generation of, genetic differences. It is defined as "the formation of individuals differing in genotype, or the presence of genotypically different individuals, in contrast to environmentally induced differences which, as a rule, cause only temporary, nonheritable changes of the phenotype". Genetic variability in a population is important for biodiversity. There are many sources of genetic variability in a population: Homologous recombination is a significant source of variability.

Spaghetti code

Spaghetti code is a pejorative phrase for unstructured and difficult-to-maintain source code. Spaghetti code can be caused by several factors, such as volatile project requirements, lack of programming style rules, and software engineers with insufficient ability or experience. Code that overuses GOTO statements rather than structured programming constructs, resulting in convoluted and unmaintainable programs, is often called spaghetti code.

Correspondence analysis

Correspondence analysis (CA) is a multivariate statistical technique proposed by Herman Otto Hartley (Hirschfeld) and later developed by Jean-Paul Benzécri. It is conceptually similar to principal component analysis, but applies to categorical rather than continuous data. In a similar manner to principal component analysis, it provides a means of displaying or summarising a set of data in two-dimensional graphical form. Its aim is to display in a biplot any structure hidden in the multivariate setting of the data table.