Palantir's 'Blueprint' for 'Shiny' Apps

Easily use 'Blueprint', the popular 'React' library from Palantir, in your 'Shiny' app. 'Blueprint' provides a rich set of UI components for creating visually appealing applications and is optimized for building complex, data-dense web interfaces. This package provides most components from the underlying library, as well as special wrappers for some components to make it easy to use them in 'R' without writing 'JavaScript' code.

Mock Data Generator

Generate mock data in R using YAML configuration.

Make Methods for R6 Classes

Generate boilerplate code for R6 classes. Given R6 class create getters and/or setters for selected class fields or use RStudio addins to insert methods straight into class definition.

Assets for 'shiny.semantic'

Style sheets and JavaScript assets for 'shiny.semantic' package.

Automated Feature Selection from 'caret'

Automated feature selection using variety of models provided by 'caret' package. This work was funded by Poland-Singapore bilateral cooperation project no 2/3/POL-SIN/2012.

Multidimensional Top Scoring for Creativity Research

Implementation of Multidimensional Top Scoring method for creativity assessment proposed in Boris Forthmann, Maciej Karwowski, Roger E. Beaty (2023) .

R Interface for H2O Sparkling Water

An extension package for 'sparklyr' that provides an R interface to H2O Sparkling Water machine learning library (see < https://github.com/h2oai/sparkling-water> for more information).

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)) .

'shiny' Info

Displays simple diagnostic information of the 'shiny' project in the user interface of the app.

Measurement Error Modelling using MCEM

Fits measurement error models using Monte Carlo Expectation Maximization (MCEM). For specific details on the methodology, see: Greg C. G. Wei & Martin A. Tanner (1990) A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, 85:411, 699-704 For more examples on measurement error modelling using MCEM, see the 'RMarkdown' vignette: "'refitME' R-package tutorial".