2. Plot manageable subsets of data- Plotting a large data set is difficult if it does not fit into memory as a data frame, so we introduce more efficient data structures and alternative plot functions that work with large data.
3. Mixed-data clustering and visualization- The aim of the project is the development of utilities for clustering subjects and variables of mixed data types. The main purpose is the generation of a mixed-data heatmap.
4. Vennerable- OBSOLETE.
Development of Vennerable has moved to https://github.com/js229/Vennerable
Vennerable provides Venn diagrams in R. It displays Venn and Euler diagrams for up to 9 different sets and using a variety of geometries
6. geneNetBP- The geneNetBP package implements methods to predict, quantify and visualize system-wide changes in beliefs after absorbing evidence in genotype-phenotype networks inferred from eQTL data.
7. rm.plot- rm.plot (for raw-means-plot) is a function for visualizing results of experimental designs with up to two factors. It plots both raw data (background) and factor means (foreground) to provide a more accurate visualization of the underlying distribution.
Development Status : 5 - Production/Stable [Filter]
14. WZD - blog package- This package goes along with the blog wagezudenken.blogspot.com, which will feature datasets from various topics, data visualizations including cartography, statistical methods and tools. The first R function implemented is a Sweave driver for bloggers.
15. Complex Data Visualization- The compDataViz package visualizes datasets with a mix of numeric and categorical data with the ability to stratify these matrices according to a categorical variable. Results of univariate and bivariate statistical tests can also be displayed.
17. Visualization methods for raster- The raster package defines classes and methods for spatial raster data access and manipulation. The rasterVis package complements raster providing a set of methods for enhanced visualization and interaction.