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If-then-else flow diagram A nested if–then–else flow diagram. In computer science, conditionals (that is, conditional statements, conditional expressions and conditional constructs) are programming language constructs that perform different computations or actions or return different values depending on the value of a Boolean expression, called a condition.
The syntax of the IIf function is as follows: IIf(expr, truepart, falsepart) All three parameters are required: e expr is the expression that is to be evaluated. truepart defines what the IIf function returns if the evaluation of expr returns true. falsepart defines what the IIf function returns if the evaluation of expr returns false.
One application of multilevel modeling (MLM) is the analysis of repeated measures data. Multilevel modeling for repeated measures data is most often discussed in the context of modeling change over time (i.e. growth curve modeling for longitudinal designs); however, it may also be used for repeated measures data in which time is not a factor.
nested blocks of imperative source code such as nested if-clauses, while-clauses, repeat-until clauses etc. information hiding: nested function definitions with lexical scope; nested data structures such as records, objects, classes, etc. nested virtualization, also called recursive virtualization: running a virtual machine inside another ...
Mondrian – data analysis tool using interactive statistical graphics with a link to R; Neurophysiological Biomarker Toolbox – Matlab toolbox for data-mining of neurophysiological biomarkers; OpenBUGS; OpenEpi – A web-based, open-source, operating-independent series of programs for use in epidemiology and statistics based on JavaScript and ...
QtiPlot is a data analysis and scientific visualisation program, similar to Origin. ROOT is a free object-oriented multi-purpose data-analysis package, developed at CERN . Salome is a free software tool that provides a generic platform for pre- and post-processing for numerical simulation.
Integrated nested Laplace approximations (INLA) is a method for approximate Bayesian inference based on Laplace's method. [1] It is designed for a class of models called latent Gaussian models (LGMs), for which it can be a fast and accurate alternative for Markov chain Monte Carlo methods to compute posterior marginal distributions.
Maximum likelihood estimation (MLE) is a standard statistical tool for finding parameter values (e.g. the unmixing matrix ) that provide the best fit of some data (e.g., the extracted signals ) to a given a model (e.g., the assumed joint probability density function (pdf) of source signals).