Classification and Regression Tests

Provides wrapper functions for running classification and regression tests using different machine learning techniques, such as Random Forests and decision trees. The package provides standardized methods for preparing data to suit the algorithm's needs, training a model, making predictions, and evaluating results. Also, some functions are provided to run multiple instances of a test.

'BERTopic'-Style Topic Modeling Without 'Python'

Implements the 'BERTopic' topic modeling pipeline directly in R: transformer-based sentence embedding, Uniform Manifold Approximation and Projection dimensionality reduction, Hierarchical Density-Based Spatial Clustering of Applications with Noise clustering, and class-based term frequency-inverse document frequency topic extraction - all without any dependency on 'Python', 'conda', or 'reticulate'. Every stage runs in R through 'torch', 'safetensors', 'tok', 'uwot', and 'dbscan'. The package mirrors the accessor API of the original 'Python' package, adds integrated quality metrics and hyperparameter search tools, and introduces part-of-speech filtered and C-value-ranked representation models.

Perform Phylogenetic Path Analysis

A comprehensive and easy to use R implementation of confirmatory phylogenetic path analysis as described by Von Hardenberg and Gonzalez-Voyer (2012) . Note that the required package 'ggm' depends on 'graph' from Bioconductor, which is not installed automatically; the simplest route is install.packages("BiocManager"); BiocManager::install("phylopath").

Minimization Methods for Ill-Conditioned Problems

Implementation of methods for minimizing ill-conditioned problems. Currently only includes regularized (quasi-)newton optimization (Kanzow and Steck et al. (2023), ).

Gene Cluster Visualizations

Provides tools for plotting gene clusters and transcripts by importing data from GenBank, FASTA, and GFF files. It performs BLASTP and MUMmer alignments [Altschul et al. (1990) ; Delcher et al. (1999) ] and displays results on gene arrow maps. Extensive customization options are available, including legends, labels, annotations, scales, colors, tooltips, and more.

'shiny' Cron Expression Input Widget

A widget for 'shiny' apps to handle schedule expression input, using the 'cron-expression-input' JavaScript component. Note that this does not edit the 'crontab' file, it is just an input element for the schedules. See < https://github.com/DatalabFabriek/shinycroneditor/blob/main/inst/examples/shiny-app.R> for an example implementation.

Context-Aware AI Assistant for 'RStudio'

A context-aware AI assistant for 'RStudio' that works directly in the source editor without switching context or opening a separate chat window. Reads the cursor position and selection to automatically choose the right action - generate, complete, continue, edit, or comment. Supports configuration of multiple large language model providers and can be invoked via the keyboard shortcut or Addins menu.

Predictive Power Score

The Predictive Power Score (PPS) is an asymmetric, data-type-agnostic score that can detect linear or non-linear relationships between two variables. The score ranges from 0 (no predictive power) to 1 (perfect predictive power). PPS can be useful for data exploration purposes, in the same way correlation analysis is. For more information on PPS, see < https://github.com/paulvanderlaken/ppsr>.

Download and Tidy Data from the Australian Prudential Regulation Authority

Download the latest data from the Australian Prudential Regulation Authority < https://www.apra.gov.au/> and import it into R as a tidy data frame.

Rasterize Layers for 'ggplot2'

Rasterize only specific layers of a 'ggplot2' plot while simultaneously keeping all labels and text in vector format. This allows users to keep plots within the reasonable size limit without loosing vector properties of the scale-sensitive information.