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MCA methods provide flexible tools, especially for case-wise analyses when the cases vary in their characteristics and needs.
Penalized regression methods provide flexible model selection tools.
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The versatility of this method provides flexible assay design and high accuracy with excellent sample throughput.
This method provides flexible non-linear mathematical function mapping of a set of input variables into the output of network.
The method provides flexible patterning in a wide range of tuned wettability surfaces in microchannels even after channel fabrication and it can be applied to various two- or multi-phase microfluidic systems.
Bayesian methods provide a flexible framework in which to summarize the joint distribution of benefit and risk.
Bayesian methods provide a flexible framework for smoothing rates and representing statistical uncertainty in the MI ratios.
Stochastic wrapper methods provide a flexible alternative and may be more powerful when the assumptions of the filter approach are not valid (Greene et al., 2009a).
RDF(S), OWL, and SPARQL provide flexible methods to represent and query properties of chemical substances.
Such norms provide flexible boundaries.
Mixed effects methods provide a more flexible modeling framework, as repeated measurements from the same individual are easily handled and missing observations from an individual do not require the entire set of individual measurements to be removed from the analysis.
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