Markov Chain Monte Carlo (MCMC) Package

Contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return 'coda' mcmc objects that can then be summarized using the 'coda' package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided.

A Toolbox for Manipulating and Assessing Colors and Palettes

Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB, and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with corresponding ggplot2 color scales. Color palette choice is aided by an interactive app (with either a Tcl/Tk or a shiny graphical user interface) and shiny apps with an HCL color picker and a color vision deficiency emulator. Plotting functions for displaying and assessing palettes include color swatches, visualizations of the HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation functions include: desaturation, lightening/darkening, mixing, and simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly). Details can be found on the project web page at < https://colorspace.R-Forge.R-project.org/> and in the accompanying scientific paper: Zeileis et al. (2020, Journal of Statistical Software, ).

Tools for Social Network Analysis

A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization.

Create Interactive 3D Visualizations of Molecular Data

Create rich and fully interactive 3D visualizations of molecular data. Visualizations can be included in Shiny apps and R markdown documents, or viewed from the R console and 'RStudio' Viewer. 'r3dmol' includes an extensive API to manipulate the visualization after creation, and supports getting data out of the visualization into R. Based on the '3dmol.js' and the 'htmlwidgets' R package.

Wrangle, Analyze, and Visualize Animal Movement Data

Tools to import, clean, and visualize movement data, particularly from motion capture systems such as Optitrack's 'Motive', the Straw Lab's 'Flydra', or from other sources. We provide functions to remove artifacts, standardize tunnel position and tunnel axes, select a region of interest, isolate specific trajectories, fill gaps in trajectory data, and calculate 3D and per-axis velocity. For experiments of visual guidance, we also provide functions that use subject position to estimate perception of visual stimuli.

Framework for Easy Statistical Modeling, Visualization, and Reporting

A meta-package that installs and loads a set of packages from 'easystats' ecosystem in a single step. This collection of packages provide a unifying and consistent framework for statistical modeling, visualization, and reporting. Additionally, it provides articles targeted at instructors for teaching 'easystats', and a dashboard targeted at new R users for easily conducting statistical analysis by accessing summary results, model fit indices, and visualizations with minimal programming.

Utility Functions and Data Sets for Data Visualization

Supporting materials for a course and book on data visualization. It contains utility functions for graphs and several sample data sets. See Healy (2019) .

Visualize Draws from the Prior and Posterior Distributions

Interactive visualization for Bayesian prior and posterior distributions. This package facilitates an animated transition between prior and posterior distributions. Additionally, it splits the distribution into bars based on the provided 'breaks,' displaying the probability for each region. If no 'breaks' are provided, it defaults to zero.

Visualize the Climate Scenario Alignment of a Financial Portfolio

Create plots to visualize the alignment of a corporate lending financial portfolio to climate change scenarios based on climate indicators (production and emission intensities) across key climate relevant sectors of the 'PACTA' methodology (Paris Agreement Capital Transition Assessment; < https://www.transitionmonitor.com/>). Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals.

Visualise Correlations

An investigative tool designed to help users visualize correlations between variables in their datasets. This package aims to provide an easy and effective way to explore and visualize these correlations, making it easier to interpret and communicate results.