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Introduction to R01:11

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R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
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Sequence Networks of Rotating Machines01:24

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Behavioral Genetics and Its Designs01:23

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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
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Related Experiment Video

Updated: Sep 5, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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An Open-Source Relational Network Derivation Script in R for Modeling and Visualizing Complex Behavior for Scientists

Patrick Smith1, Steven C Hayes1

  • 1Department of Psychology, University of Nevada, Reno, Reno, NV, United States.

Frontiers in Psychology
|July 5, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces novel R language tools for modeling relational cognition, enhancing transparency in artificial intelligence and computational cognitive science. These methods facilitate exploration of complex relational models for scientific advancement.

Keywords:
Rgraph networksrelational derivationrelational frame theoryrelational networks

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Relational models offer parsimonious explanations for human generative behavior.
  • Current computational cognitive models, including Deep Neural Networks and Reinforcement Learning, are informed by relational principles.
  • The 'black box' nature of computational models hinders scientific and applied progress.

Purpose of the Study:

  • To present a novel approach for modeling relational cognitive processes.
  • To introduce tools for exploring complex relational models in cognitive science.
  • To bridge the gap between theoretical cognitive science and advanced computational models.

Main Methods:

  • Development of logical derivation scripts in the R language.
  • Creation of network graph visualizations using the R language.
  • Application of these tools to model relational processes in cognition.

Main Results:

  • Demonstration of a novel method for modeling relational cognition.
  • Provision of open-source R tools for researchers and practitioners.
  • Facilitation of exploration into complex relational dynamics.

Conclusions:

  • The developed R tools offer a transparent method for investigating relational cognition.
  • These tools can advance both the theory and empirical science of cognition.
  • The approach prepares the field for future integration with advanced computational cognitive models.