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Data Visualization for Statistics in Social Science
Collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, principal component analysis and correlation matrices, cluster analyses, scatter plots, stacked scales, effects plots of regression models (including interaction terms) and much more. This package supports labelled data.
Visualization and Analysis Tools for Neural Networks
Visualization and analysis tools to aid in the interpretation of neural network models. Functions are available for plotting, quantifying variable importance, conducting a sensitivity analysis, and obtaining a simple list of model weights.
Interactive Visual and Numerical Diagnostics and Posterior Analysis for Bayesian Models
A graphical user interface for interactive Markov chain Monte Carlo (MCMC) diagnostics and plots and tables helpful for analyzing a posterior sample. The interface is powered by the 'Shiny' web application framework from 'RStudio' and works with the output of MCMC programs written in any programming language (and has extended functionality for 'Stan' models fit using the 'rstan' and 'rstanarm' packages).
Additional Layout Algorithms for Network Visualizations
Several new layout algorithms to visualize networks are provided which are not part of 'igraph'.
Most are based on the concept of stress majorization by Gansner et al. (2004)
Summarise and Visualise Characteristics of Patients in the OMOP CDM
Summarise and visualise the characteristics of patients in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model (CDM).
Graphical Web-Framework for Data Manipulation and Visualization
A framework for data manipulation and visualization using a web-based point and click user interface where analysis pipelines are decomposed into re-usable and parameterizable blocks.
Easy Analysis and Visualization of Factorial Experiments
Facilitates easy analysis of factorial experiments, including purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss designs, and mixed within-and-between-Ss designs. The functions in this package aim to provide simple, intuitive and consistent specification of data analysis and visualization. Visualization functions also include design visualization for pre-analysis data auditing, and correlation matrix visualization. Finally, this package includes functions for non-parametric analysis, including permutation tests and bootstrap resampling. The bootstrap function obtains predictions either by cell means or by more advanced/powerful mixed effects models, yielding predictions and confidence intervals that may be easily visualized at any level of the experiment's design.
Visualizing Association Rules and Frequent Itemsets
Extends package 'arules' with various visualization
techniques for association rules and itemsets. The package also
includes several interactive visualizations for rule exploration.
Michael Hahsler (2017)
Interpretable Bivariate Density Visualization with 'ggplot2'
The 'ggplot2' package provides simple functions for visualizing contours of 2-d kernel density estimates. 'ggdensity' implements several additional density estimators as well as more interpretable visualizations based on highest density regions instead of the traditional height of the estimated density surface.
Grammar of Graphics and Plot Helpers for Time Series Visualization
Extends the capabilities of 'ggplot2' by providing grammatical elements and plot helpers designed for visualizing temporal patterns. The package implements a grammar of temporal graphics, which leverages calendar structures to highlight changes over time. The package also provides plot helper functions to quickly produce commonly used time series graphics, including time plots, season plots, and seasonal sub-series plots.