Easily translate 'ggplot2' graphs to an interactive web-based version and/or create custom web-based visualizations directly from R. Once uploaded to a 'plotly' account, 'plotly' graphs (and the data behind them) can be viewed and modified in a web browser.

Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, ...

Provides generators for a high number of both single- and multi- objective test functions which are frequently used for the benchmarking of (numerical) optimization algorithms. Moreover, it offers a set of convenient functions to generate, plot and ...

A collection of various visualizations methods is provided. With regards to high-dimensional data the heat map and silhouette plot for grouped data,visualizations of the distribution of distances, the scatter-density plot for two variables,the ...

Create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using 'ggplot2'.

The bioinformatic evaluation of gene co-expression often begins with correlation-based analyses. However, this approach lacks statistical validity when applied to relative data.

Network visualization in the 'ggplot2' framework. Network functionality is provided in a single 'ggplot2' layer by calling the geom 'net'.

Like package 'manipulate' does for static graphics, this package helps to easily add controls like sliders, pickers, checkboxes, etc. that can be used to modify the input data or the parameters of an interactive chart created with package ...

flowcatchR is a set of tools to analyze in vivo microscopy imaging data, focused on tracking flowing blood cells. It guides the steps from segmentation to calculation of features, filtering out particles not of interest, providing also a set of ...

Functions for calculating and visualizing growth-rate inhibition (GR) metrics.

A simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample's gene expression profile. It scores the expression activities of gene sets at a single-sample level.