Your Advanced 3D Brain Visualization

A fast, interactive cross-platform, and easy to share 'WebGL'-based 3D brain viewer that visualizes 'FreeSurfer' and/or 'AFNI/SUMA' surfaces. The viewer widget can be either standalone or embedded into 'R-shiny' applications. The standalone version only require a web browser with 'WebGL2' support (for example, 'Chrome', 'Firefox', 'Safari'), and can be inserted into any websites. The 'R-shiny' support allows the 3D viewer to be dynamically generated from reactive user inputs. Please check the publication by Wang, Magnotti, Zhang, and Beauchamp (2023, ) for electrode localization. This viewer has been fully adopted by 'RAVE' < https://openwetware.org/wiki/RAVE>, an interactive toolbox to analyze 'iEEG' data by Magnotti, Wang, and Beauchamp (2020, ). Please check 'citation("threeBrain")' for details.

Projection Pursuit Classification Tree Visualization

Tools for exploring projection pursuit classification tree using various projection pursuit indexes.

Interactive Viewing of Spatial Data in R

Quickly and conveniently create interactive visualisations of spatial data with or without background maps. Attributes of displayed features are fully queryable via pop-up windows. Additional functionality includes methods to visualise true- and false-color raster images and bounding boxes.

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.

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.

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.

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.

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.

Flexible Tool for Bias Detection, Visualization, and Mitigation

Measure fairness metrics in one place for many models. Check how big is model's bias towards different races, sex, nationalities etc. Use measures such as Statistical Parity, Equal odds to detect the discrimination against unprivileged groups. Visualize the bias using heatmap, radar plot, biplot, bar chart (and more!). There are various pre-processing and post-processing bias mitigation algorithms implemented. Package also supports calculating fairness metrics for regression models. Find more details in (Wiśniewski, Biecek (2021)) .